packaging: cleanup: remove non-existing paths

The examples have lived below gnpy/example-data/ for a few releases
already, and I've checked that these files are included in release
tarballs and wheels.

Change-Id: I782fc56a171f4cbc08f6698ac5d339e4cacecbb4

.github/workflows/main.yml

@@ -26,7 +26,8 @@ jobs:
       matrix:
         tox_env:
           - py38
-          - py39-cover
+          - py39
+          - py310-cover
         include:
           - tox_env: docs
             dnf_install: graphviz
@@ -43,7 +44,7 @@ jobs:
       - uses: actions/setup-python@v2
         name: Install Python
         with:
-          python-version: '3.9'
+          python-version: '3.10'
       - uses: casperdcl/deploy-pypi@bb869aafd89f657ceaafe9561d3b5584766c0f95
         with:
           password: ${{ secrets.PYPI_API_TOKEN }}
@@ -103,9 +104,9 @@ jobs:
           python-version: ${{ matrix.python_version }}
       - run: |
           pip install --editable .
-          pip install 'pytest>=5.0.0,<6'
+          pip install 'pytest>=6.2.5,<7'
           pytest -vv
     strategy:
       matrix:
         python_version:
-          - "3.9"
+          - "3.10"

.lgtm.yml

@@ -0,0 +1,3 @@
+queries:
+- exclude: py/clear-text-logging-sensitive-data
+- exclude: py/clear-text-storage-sensitive-data

.zuul.yaml

@@ -2,25 +2,21 @@
 - project:
     check:
       jobs:
-        - tox-py38-f34
-        - tox-py39-cover
-        - tox-docs-f34
+        - tox-py38
+        - tox-py39
+        - tox-py310-cover
+        - tox-docs-f35
         - coverage-diff:
             voting: false
             dependencies:
-              - tox-py39-cover-previous
-              - tox-py39-cover
+              - tox-py310-cover-previous
+              - tox-py310-cover
             vars:
-              coverage_job_name_previous: tox-py39-cover-previous
-              coverage_job_name_current: tox-py39-cover
+              coverage_job_name_previous: tox-py310-cover-previous
+              coverage_job_name_current: tox-py310-cover
         - tox-linters-diff-n-report:
             voting: false
-        - tox-py39-cover-previous
-    gate:
-      jobs:
-        - tox-py38-f34
-        - tox-py39-f34
-        - tox-docs-f34
+        - tox-py310-cover-previous
     tag:
       jobs:
         - oopt-release-python:

docs/about-project.md

@@ -7,6 +7,7 @@ There are weekly calls about our progress.
 Newcomers, users and telecom operators are especially welcome there.
 We encourage all interested people outside the TIP to [join the project](https://telecominfraproject.com/apply-for-membership/) and especially to [get in touch with us](https://github.com/Telecominfraproject/oopt-gnpy/discussions).
 
+(contributing)=
 ## Contributing
 
 `gnpy` is looking for additional contributors, especially those with experience planning and maintaining large-scale, real-world mesh optical networks.

docs/conf.py

@@ -190,3 +190,5 @@ autodoc_default_options = {
 }
 
 graphviz_output_format = 'svg'
+
+bibtex_bibfiles = ['biblio.bib']

docs/extending.rst

@@ -4,7 +4,7 @@ Extending GNPy with vendor-specific data
 ========================================
 
 GNPy ships with an :ref:`equipment library<concepts-equipment>` containing machine-readable datasheets of networking equipment.
-Vendors who are willing to contribute descriptions of their supported products are encouraged to `submit a patch <https://review.gerrithub.io/Documentation/intro-gerrit-walkthrough-github.html>`__.
+Vendors who are willing to contribute descriptions of their supported products are encouraged to `submit a patch <https://review.gerrithub.io/Documentation/intro-gerrit-walkthrough-github.html>`__ -- or just :ref:`get in touch with us directly<contributing>`.
 
 This chapter discusses option for modeling performance of :ref:`EDFA amplifiers<extending-edfa>`, :ref:`Raman amplifiers<extending-raman>`, :ref:`transponders<extending-transponder>` and :ref:`ROADMs<extending-roadm>`.
 
@@ -29,7 +29,7 @@ The NF is expressed as a third-degree polynomial:
 
        f(x) &= \text{a}x^3 + \text{b}x^2 + \text{c}x + \text{d}
 
-  \text{NF} &= f(G_\text{max} - G)
+  \text{NF} &= f(G - G_\text{max})
 
 This model can be also used for fixed-gain fixed-NF amplifiers.
 In that case, use:

docs/json.rst

@@ -73,13 +73,57 @@ The fiber library currently describes SSMF and NZDF but additional fiber types c
 | ``dispersion_slope`` | (number)  | In :math:`s \times m^{-1} \times m^{-1}  |
 |                      |           | \times m^{-1}`                           |
 +----------------------+-----------+------------------------------------------+
-| ``gamma``            | (number)  | :math:`2\pi\times n^2/(\lambda*A_{eff})`,|
-|                      |           | in :math:`w^{-1} \times m^{-1}`.         |
+| ``effective_area``   | (number)  | Effective area of the fiber (not just    |
+|                      |           | the MFD circle). This is the             |
+|                      |           | :math:`A_{eff}`, see e.g., the           |
+|                      |           | `Corning whitepaper on MFD/EA`_.         |
+|                      |           | Specified in :math:`m^{2}`.              |
++----------------------+-----------+------------------------------------------+
+| ``gamma``            | (number)  | Coefficient :math:`\gamma = 2\pi\times   |
+|                      |           | n^2/(\lambda*A_{eff})`.                  |
+|                      |           | If not provided, this will be derived    |
+|                      |           | from the ``effective_area``              |
+|                      |           | :math:`A_{eff}`.                         |
+|                      |           | In :math:`w^{-1} \times m^{-1}`.         |
 +----------------------+-----------+------------------------------------------+
 | ``pmd_coef``         | (number)  | Polarization mode dispersion (PMD)       |
 |                      |           | coefficient. In                          |
 |                      |           | :math:`s\times\sqrt{m}^{-1}`.            |
 +----------------------+-----------+------------------------------------------+
+| ``lumped_losses``    | (array)   | Places along the fiber length with extra |
+|                      |           | losses. Specified as a loss in dB at     |
+|                      |           | each relevant position (in km):          |
+|                      |           | ``{"position": 10, "loss": 1.5}``)       |
++----------------------+-----------+------------------------------------------+
+
+.. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf
+
+RamanFiber
+~~~~~~~~~~
+
+The RamanFiber can be used to simulate Raman amplification through dedicated Raman pumps. The Raman pumps must be listed
+in the key ``raman_pumps`` within the RamanFiber ``operational`` dictionary. The description of each Raman pump must
+contain the following:
+
++---------------------------+-----------+------------------------------------------------------------+
+| field                     | type      | description                                                |
++===========================+===========+============================================================+
+| ``power``                 | (number)  | Total pump power in :math:`W`                              |
+|                           |           | considering a depolarized pump                             |
++---------------------------+-----------+------------------------------------------------------------+
+| ``frequency``             | (number)  | Pump central frequency in :math:`Hz`                       |
++---------------------------+-----------+------------------------------------------------------------+
+| ``propagation_direction`` | (number)  | The pumps can propagate in the same or opposite direction  |
+|                           |           | with respect the signal. Valid choices are ``coprop`` and  |
+|                           |           | ``counterprop``, respectively                              |
++---------------------------+-----------+------------------------------------------------------------+
+
+Beside the list of Raman pumps, the RamanFiber ``operational`` dictionary must include the ``temperature`` that affects
+the amplified spontaneous emission noise generated by the Raman amplification.
+As the loss coefficient significantly varies outside the C-band, where the Raman pumps are usually placed,
+it is suggested to include an estimation of the loss coefficient for the Raman pump central frequencies within
+a dictionary-like definition of the ``RamanFiber.params.loss_coef``
+(e.g. ``loss_coef = {"value": [0.18, 0.18, 0.20, 0.20], "frequency": [191e12, 196e12, 200e12, 210e12]}``).
 
 Transceiver
 ~~~~~~~~~~~
@@ -176,6 +220,61 @@ Only the EDFA that are marked ``'allowed_for_design': true`` are considered.
 
 For amplifiers defined in the topology JSON input but whose ``gain = 0`` (placeholder), auto-design will set its gain automatically: see ``power_mode`` in the ``Spans`` library to find out how the gain is calculated.
 
+The file ``sim_params.json`` contains the tuning parameters used within both the ``gnpy.science_utils.RamanSolver`` and
+the ``gnpy.science_utils.NliSolver`` for the evaluation of the Raman profile and the NLI generation, respectively.
+
++---------------------------------------------+-----------+---------------------------------------------+
+| field                                       |   type    | description                                 |
++=============================================+===========+=============================================+
+| ``raman_params.flag``                       | (boolean) | Enable/Disable the Raman effect that        |
+|                                             |           | produces a power transfer from higher to    |
+|                                             |           | lower frequencies.                          |
+|                                             |           | In general, considering the Raman effect    |
+|                                             |           | provides more accurate results. It is       |
+|                                             |           | mandatory when Raman amplification is       |
+|                                             |           | included in the simulation                  |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.result_spatial_resolution``  | (number)  | Spatial resolution of the output            |
+|                                             |           | Raman profile along the entire fiber span.  |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the NLI               |
+|                                             |           | calculation when the GGN method is used:    |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 10e3 m                |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.solver_spatial_resolution``  | (number)  | Spatial step for the iterative solution     |
+|                                             |           | of the first order differential equation    |
+|                                             |           | used to calculate the Raman profile         |
+|                                             |           | along the entire fiber span.                |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the evaluated         |
+|                                             |           | Raman profile:                              |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 100 m                 |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.method``                       | (string)  | Model used for the NLI evaluation. Valid    |
+|                                             |           | choices are ``gn_model_analytic`` (see      |
+|                                             |           | eq. 120 from `arXiv:1209.0394               |
+|                                             |           | <https://arxiv.org/abs/1209.0394>`_) and    |
+|                                             |           | ``ggn_spectrally_separated`` (see eq. 21    |
+|                                             |           | from `arXiv:1710.02225                      |
+|                                             |           | <https://arxiv.org/abs/1710.02225>`_).      |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.computed_channels``            | (number)  | The channels on which the NLI is            |
+|                                             |           | explicitly evaluated.                       |
+|                                             |           | The NLI of the other channels is            |
+|                                             |           | interpolated using ``numpy.interp``.        |
+|                                             |           | In a C-band simulation with 96 channels in  |
+|                                             |           | a 50 GHz spacing fix-grid we recommend at   |
+|                                             |           | one computed channel every 20 channels.     |
++---------------------------------------------+-----------+---------------------------------------------+
+
 Span
 ~~~~
 

docs/model.rst

@@ -145,4 +145,4 @@ Raman Scattering in order to give a proper estimation for all channels
 :cite:`cantono2018modeling`.  This will be the main upgrade required within the
 PSE framework.
 
-.. bibliography:: biblio.bib  
+.. bibliography::

docs/requirements.txt

@@ -1,7 +1,7 @@
 alabaster>=0.7.12,<1
-docutils>=0.15.2,<1
-myst-parser>=0.14.0,<1
-Pygments>=2.7.4,<3
+docutils>=0.17.1,<1
+myst-parser>=0.16.1,<1
+Pygments>=2.11.2,<3
 rstcheck
-Sphinx>=3.5.0,<4
-sphinxcontrib-bibtex>=0.4.2,<1
+Sphinx>=4.4.0,<5
+sphinxcontrib-bibtex>=2.4.1,<3

gnpy/core/elements.py

@@ -20,13 +20,17 @@ unique identifier and a printable name, and provide the :py:meth:`__call__` meth
 instance as a result.
 """
 
-from numpy import abs, arange, array, divide, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt
+from numpy import abs, array, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt, log10, exp, asarray, full,\
+    squeeze, zeros, append, flip, outer
 from scipy.constants import h, c
+from scipy.interpolate import interp1d
 from collections import namedtuple
 
 from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum
-from gnpy.core.parameters import FiberParams, PumpParams
-from gnpy.core.science_utils import NliSolver, RamanSolver, propagate_raman_fiber, _psi
+from gnpy.core.parameters import RoadmParams, FusedParams, FiberParams, PumpParams, EdfaParams, EdfaOperational
+from gnpy.core.science_utils import NliSolver, RamanSolver
+from gnpy.core.info import SpectralInformation
+from gnpy.core.exceptions import NetworkTopologyError, SpectrumError
 
 
 class Location(namedtuple('Location', 'latitude longitude city region')):
@@ -79,32 +83,47 @@ class Transceiver(_Node):
         self.baud_rate = None
         self.chromatic_dispersion = None
         self.pmd = None
+        self.pdl = None
+        self.penalties = {}
+        self.total_penalty = 0
 
     def _calc_cd(self, spectral_info):
         """ Updates the Transceiver property with the CD of the received channels. CD in ps/nm.
         """
-        self.chromatic_dispersion = [carrier.chromatic_dispersion * 1e3 for carrier in spectral_info.carriers]
+        self.chromatic_dispersion = spectral_info.chromatic_dispersion * 1e3
 
     def _calc_pmd(self, spectral_info):
         """Updates the Transceiver property with the PMD of the received channels. PMD in ps.
         """
-        self.pmd = [carrier.pmd*1e12 for carrier in spectral_info.carriers]
+        self.pmd = spectral_info.pmd * 1e12
+
+    def _calc_pdl(self, spectral_info):
+        """Updates the Transceiver property with the PDL of the received channels. PDL in dB.
+        """
+        self.pdl = spectral_info.pdl
+
+    def _calc_penalty(self, impairment_value, boundary_list):
+        return interp(impairment_value, boundary_list['up_to_boundary'], boundary_list['penalty_value'],
+                      left=float('inf'), right=float('inf'))
+
+    def calc_penalties(self, penalties):
+        """Updates the Transceiver property with penalties (CD, PMD, etc.) of the received channels in dB.
+           Penalties are linearly interpolated between given points and set to 'inf' outside interval.
+        """
+        self.penalties = {impairment: self._calc_penalty(getattr(self, impairment), boundary_list)
+                          for impairment, boundary_list in penalties.items()}
+        self.total_penalty = sum(list(self.penalties.values()), axis=0)
 
     def _calc_snr(self, spectral_info):
         with errstate(divide='ignore'):
-            self.baud_rate = [c.baud_rate for c in spectral_info.carriers]
-            ratio_01nm = [lin2db(12.5e9 / b_rate) for b_rate in self.baud_rate]
+            self.baud_rate = spectral_info.baud_rate
+            ratio_01nm = lin2db(12.5e9 / self.baud_rate)
             # set raw values to record original calculation, before update_snr()
-            self.raw_osnr_ase = [lin2db(divide(c.power.signal, c.power.ase))
-                                 for c in spectral_info.carriers]
-            self.raw_osnr_ase_01nm = [ase - ratio for ase, ratio
-                                      in zip(self.raw_osnr_ase, ratio_01nm)]
-            self.raw_osnr_nli = [lin2db(divide(c.power.signal, c.power.nli))
-                                 for c in spectral_info.carriers]
-            self.raw_snr = [lin2db(divide(c.power.signal, c.power.nli + c.power.ase))
-                            for c in spectral_info.carriers]
-            self.raw_snr_01nm = [snr - ratio for snr, ratio
-                                 in zip(self.raw_snr, ratio_01nm)]
+            self.raw_osnr_ase = lin2db(spectral_info.signal / spectral_info.ase)
+            self.raw_osnr_ase_01nm = self.raw_osnr_ase - ratio_01nm
+            self.raw_osnr_nli = lin2db(spectral_info.signal / spectral_info.nli)
+            self.raw_snr = lin2db(spectral_info.signal / (spectral_info.ase + spectral_info.nli))
+            self.raw_snr_01nm = self.raw_snr - ratio_01nm
 
             self.osnr_ase = self.raw_osnr_ase
             self.osnr_ase_01nm = self.raw_osnr_ase_01nm
@@ -124,14 +143,10 @@ class Transceiver(_Node):
         for s in args:
             snr_added += db2lin(-s)
         snr_added = -lin2db(snr_added)
-        self.osnr_ase = list(map(lambda x, y: snr_sum(x, y, snr_added),
-                                 self.raw_osnr_ase, self.baud_rate))
-        self.snr = list(map(lambda x, y: snr_sum(x, y, snr_added),
-                            self.raw_snr, self.baud_rate))
-        self.osnr_ase_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
-                                      self.raw_osnr_ase_01nm))
-        self.snr_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
-                                 self.raw_snr_01nm))
+        self.osnr_ase = snr_sum(self.raw_osnr_ase, self.baud_rate, snr_added)
+        self.snr = snr_sum(self.raw_snr, self.baud_rate, snr_added)
+        self.osnr_ase_01nm = snr_sum(self.raw_osnr_ase_01nm, 12.5e9, snr_added)
+        self.snr_01nm = snr_sum(self.raw_snr_01nm, 12.5e9, snr_added)
 
     @property
     def to_json(self):
@@ -150,7 +165,9 @@ class Transceiver(_Node):
                 f'osnr_nli={self.osnr_nli!r}, '
                 f'snr={self.snr!r}, '
                 f'chromatic_dispersion={self.chromatic_dispersion!r}, '
-                f'pmd={self.pmd!r})')
+                f'pmd={self.pmd!r}, '
+                f'pdl={self.pdl!r}, '
+                f'penalties={self.penalties!r})')
 
     def __str__(self):
         if self.snr is None or self.osnr_ase is None:
@@ -162,34 +179,46 @@ class Transceiver(_Node):
         snr_01nm = round(mean(self.snr_01nm), 2)
         cd = mean(self.chromatic_dispersion)
         pmd = mean(self.pmd)
+        pdl = mean(self.pdl)
 
-        return '\n'.join([f'{type(self).__name__} {self.uid}',
+        result = '\n'.join([f'{type(self).__name__} {self.uid}',
 
                           f'  GSNR (0.1nm, dB):          {snr_01nm:.2f}',
                           f'  GSNR (signal bw, dB):      {snr:.2f}',
                           f'  OSNR ASE (0.1nm, dB):      {osnr_ase_01nm:.2f}',
                           f'  OSNR ASE (signal bw, dB):  {osnr_ase:.2f}',
                           f'  CD (ps/nm):                {cd:.2f}',
-                          f'  PMD (ps):                  {pmd:.2f}'])
+                          f'  PMD (ps):                  {pmd:.2f}',
+                          f'  PDL (dB):                  {pdl:.2f}'])
+
+        cd_penalty = self.penalties.get('chromatic_dispersion')
+        if cd_penalty is not None:
+            result += f'\n  CD penalty (dB):           {mean(cd_penalty):.2f}'
+        pmd_penalty = self.penalties.get('pmd')
+        if pmd_penalty is not None:
+            result += f'\n  PMD penalty (dB):          {mean(pmd_penalty):.2f}'
+        pdl_penalty = self.penalties.get('pdl')
+        if pdl_penalty is not None:
+            result += f'\n  PDL penalty (dB):          {mean(pdl_penalty):.2f}'
+
+        return result
 
     def __call__(self, spectral_info):
         self._calc_snr(spectral_info)
         self._calc_cd(spectral_info)
         self._calc_pmd(spectral_info)
+        self._calc_pdl(spectral_info)
         return spectral_info
 
 
-RoadmParams = namedtuple('RoadmParams', 'target_pch_out_db add_drop_osnr pmd restrictions per_degree_pch_out_db')
-
-
 class Roadm(_Node):
-    def __init__(self, *args, params, **kwargs):
-        if 'per_degree_pch_out_db' not in params.keys():
-            params['per_degree_pch_out_db'] = {}
+    def __init__(self, *args, params=None, **kwargs):
+        if not params:
+            params = {}
         super().__init__(*args, params=RoadmParams(**params), **kwargs)
+        self.pch_out_db = self.params.target_pch_out_db
         self.loss = 0  # auto-design interest
         self.effective_loss = None
-        self.effective_pch_out_db = self.params.target_pch_out_db
         self.passive = True
         self.restrictions = self.params.restrictions
         self.per_degree_pch_out_db = self.params.per_degree_pch_out_db
@@ -199,7 +228,7 @@ class Roadm(_Node):
         return {'uid': self.uid,
                 'type': type(self).__name__,
                 'params': {
-                    'target_pch_out_db': self.effective_pch_out_db,
+                    'target_pch_out_db': self.pch_out_db,
                     'restrictions': self.restrictions,
                     'per_degree_pch_out_db': self.per_degree_pch_out_db
                     },
@@ -217,9 +246,9 @@ class Roadm(_Node):
 
         return '\n'.join([f'{type(self).__name__} {self.uid}',
                           f'  effective loss (dB):  {self.effective_loss:.2f}',
-                          f'  pch out (dBm):        {self.effective_pch_out_db:.2f}'])
+                          f'  pch out (dBm):        {self.pch_out_db:.2f}'])
 
-    def propagate(self, pref, *carriers, degree):
+    def propagate(self, spectral_info, degree):
         # pin_target and loss are read from eqpt_config.json['Roadm']
         # all ingress channels in xpress are set to this power level
         # but add channels are not, so we define an effective loss
@@ -229,38 +258,31 @@ class Roadm(_Node):
         # if the input power is lower than the target one, use the input power instead because
         # a ROADM doesn't amplify, it can only attenuate
         # TODO maybe add a minimum loss for the ROADM
-        per_degree_pch = self.per_degree_pch_out_db[degree] if degree in self.per_degree_pch_out_db.keys() else self.params.target_pch_out_db
-        self.effective_pch_out_db = min(pref.p_spani, per_degree_pch)
-        self.effective_loss = pref.p_spani - self.effective_pch_out_db
-        carriers_power = array([c.power.signal + c.power.nli + c.power.ase for c in carriers])
-        carriers_att = list(map(lambda x: lin2db(x * 1e3) - per_degree_pch, carriers_power))
-        exceeding_att = -min(list(filter(lambda x: x < 0, carriers_att)), default=0)
-        carriers_att = list(map(lambda x: db2lin(x + exceeding_att), carriers_att))
-        for carrier_att, carrier in zip(carriers_att, carriers):
-            pwr = carrier.power
-            pwr = pwr._replace(signal=pwr.signal / carrier_att,
-                               nli=pwr.nli / carrier_att,
-                               ase=pwr.ase / carrier_att)
-            pmd = sqrt(carrier.pmd**2 + self.params.pmd**2)
-            yield carrier._replace(power=pwr, pmd=pmd)
+        per_degree_pch = self.per_degree_pch_out_db[degree] \
+            if degree in self.per_degree_pch_out_db else self.pch_out_db
+        self.pch_out_db = min(spectral_info.pref.p_spani, per_degree_pch)
+        self.effective_loss = spectral_info.pref.p_spani - self.pch_out_db
+        input_power = spectral_info.signal + spectral_info.nli + spectral_info.ase
+        min_power = min(lin2db(input_power * 1e3))
+        per_degree_pch = per_degree_pch if per_degree_pch < min_power else min_power
+        delta_power = lin2db(input_power * 1e3) - per_degree_pch
+        spectral_info.apply_attenuation_db(delta_power)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
+        spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
 
-    def update_pref(self, pref):
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.effective_pch_out_db)
+    def update_pref(self, spectral_info):
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0, p_spani=self.pch_out_db)
 
     def __call__(self, spectral_info, degree):
-        carriers = tuple(self.propagate(spectral_info.pref, *spectral_info.carriers, degree=degree))
-        pref = self.update_pref(spectral_info.pref)
-        return spectral_info._replace(carriers=carriers, pref=pref)
-
-
-FusedParams = namedtuple('FusedParams', 'loss')
+        self.propagate(spectral_info, degree=degree)
+        self.update_pref(spectral_info)
+        return spectral_info
 
 
 class Fused(_Node):
     def __init__(self, *args, params=None, **kwargs):
-        if params is None:
-            # default loss value if not mentioned in loaded network json
-            params = {'loss': 1}
+        if not params:
+            params = {}
         super().__init__(*args, params=FusedParams(**params), **kwargs)
         self.loss = self.params.loss
         self.passive = True
@@ -284,23 +306,17 @@ class Fused(_Node):
         return '\n'.join([f'{type(self).__name__} {self.uid}',
                           f'  loss (dB): {self.loss:.2f}'])
 
-    def propagate(self, *carriers):
-        attenuation = db2lin(self.loss)
+    def propagate(self, spectral_info):
+        spectral_info.apply_attenuation_db(self.loss)
 
-        for carrier in carriers:
-            pwr = carrier.power
-            pwr = pwr._replace(signal=pwr.signal / attenuation,
-                               nli=pwr.nli / attenuation,
-                               ase=pwr.ase / attenuation)
-            yield carrier._replace(power=pwr)
-
-    def update_pref(self, pref):
-        return pref._replace(p_span0=pref.p_span0, p_spani=pref.p_spani - self.loss)
+    def update_pref(self, spectral_info):
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
+                                                         p_spani=spectral_info.pref.p_spani - self.loss)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(*spectral_info.carriers))
-        pref = self.update_pref(spectral_info.pref)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        self.propagate(spectral_info)
+        self.update_pref(spectral_info)
+        return spectral_info
 
 
 class Fiber(_Node):
@@ -309,7 +325,28 @@ class Fiber(_Node):
             params = {}
         super().__init__(*args, params=FiberParams(**params), **kwargs)
         self.pch_out_db = None
-        self.nli_solver = NliSolver(self)
+        self.passive = True
+
+        # Raman efficiency matrix function of the delta frequency constructed such that each row is related to a
+        # fixed frequency: positive elements represent a gain (from higher frequency) and negative elements represent
+        # a loss (to lower frequency)
+        if self.params.raman_efficiency:
+            frequency_offset = self.params.raman_efficiency['frequency_offset']
+            frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
+            cr = self.params.raman_efficiency['cr']
+            cr = append(- flip(cr[1:]), cr)
+            self._cr_function = lambda frequency: interp(frequency, frequency_offset, cr)
+        else:
+            self._cr_function = lambda frequency: zeros(squeeze(frequency).shape)
+
+        # Lumped losses
+        z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses])  # km
+        lumped_losses_power = array([lumped['loss'] for lumped in self.params.lumped_losses])  # dB
+        if not ((z_lumped_losses > 0) * (z_lumped_losses < 1e-3 * self.params.length)).all():
+            raise NetworkTopologyError("Lumped loss positions must be between 0 and the fiber length "
+                                       f"({1e-3 * self.params.length} km), boundaries excluded.")
+        self.lumped_losses = db2lin(- lumped_losses_power)  # [linear units]
+        self.z_lumped_losses = array(z_lumped_losses) * 1e3  # [m]
 
     @property
     def to_json(self):
@@ -319,7 +356,7 @@ class Fiber(_Node):
                 'params': {
                     # have to specify each because namedtupple cannot be updated :(
                     'length': round(self.params.length * 1e-3, 6),
-                    'loss_coef': self.params.loss_coef * 1e3,
+                    'loss_coef': round(self.params.loss_coef * 1e3, 6),
                     'length_units': 'km',
                     'att_in': self.params.att_in,
                     'con_in': self.params.con_in,
@@ -348,43 +385,56 @@ class Fiber(_Node):
                           f'  (conn loss out includes EOL margin defined in eqpt_config.json)',
                           f'  pch out (dBm): {self.pch_out_db:.2f}'])
 
+    def loss_coef_func(self, frequency):
+        frequency = asarray(frequency)
+        if self.params.loss_coef.size > 1:
+            try:
+                loss_coef = interp1d(self.params.f_loss_ref, self.params.loss_coef)(frequency)
+            except ValueError:
+                raise SpectrumError('The spectrum bandwidth exceeds the frequency interval used to define the fiber '
+                                    f'loss coefficient in "{type(self).__name__} {self.uid}".'
+                                    f'\nSpectrum f_min-f_max: {round(frequency[0]*1e-12,2)}-'
+                                    f'{round(frequency[-1]*1e-12,2)}'
+                                    f'\nLoss coefficient f_min-f_max: {round(self.params.f_loss_ref[0]*1e-12,2)}-'
+                                    f'{round(self.params.f_loss_ref[-1]*1e-12,2)}')
+        else:
+            loss_coef = full(frequency.size, self.params.loss_coef)
+        return squeeze(loss_coef)
+
+
     @property
     def loss(self):
         """total loss including padding att_in: useful for polymorphism with roadm loss"""
-        return self.params.loss_coef * self.params.length + self.params.con_in + self.params.con_out + self.params.att_in
+        return self.loss_coef_func(self.params.ref_frequency) * self.params.length + \
+            self.params.con_in + self.params.con_out + self.params.att_in
 
-    @property
-    def passive(self):
-        return True
+    def alpha(self, frequency):
+        """Returns the linear exponent attenuation coefficient such that
+        :math: `lin_attenuation = e^{- alpha length}`
 
-    def alpha(self, frequencies):
-        """It returns the values of the series expansion of attenuation coefficient alpha(f) for all f in frequencies
-
-        :param frequencies: frequencies of series expansion [Hz]
-        :return: alpha: power attenuation coefficient for f in frequencies [Neper/m]
+        :param frequency: the frequency at which alpha is computed [Hz]
+        :return: alpha: power attenuation coefficient for f in frequency [Neper/m]
         """
-        if type(self.params.loss_coef) == dict:
-            alpha = interp(frequencies, self.params.f_loss_ref, self.params.lin_loss_exp)
-        else:
-            alpha = self.params.lin_loss_exp * ones(frequencies.shape)
+        return self.loss_coef_func(frequency) / (10 * log10(exp(1)))
 
-        return alpha
+    def cr(self, frequency):
+        """Returns the raman efficiency matrix including the vibrational loss
 
-    def alpha0(self, f_ref=193.5e12):
-        """It returns the zero element of the series expansion of attenuation coefficient alpha(f) in the
-        reference frequency f_ref
-
-        :param f_ref: reference frequency of series expansion [Hz]
-        :return: alpha0: power attenuation coefficient in f_ref [Neper/m]
+        :param frequency: the frequency at which cr is computed [Hz]
+        :return: cr: raman efficiency matrix [1 / (W m)]
         """
-        return self.alpha(f_ref * ones(1))[0]
+        df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
+        cr = self._cr_function(df)
+        vibrational_loss = outer(frequency, ones(frequency.shape)) / outer(ones(frequency.shape), frequency)
+        return cr * (cr >= 0) + cr * (cr < 0) * vibrational_loss  # Raman efficiency [1/(W m)]
 
-    def chromatic_dispersion(self, freq=193.5e12):
+    def chromatic_dispersion(self, freq=None):
         """Returns accumulated chromatic dispersion (CD).
 
         :param freq: the frequency at which the chromatic dispersion is computed
         :return: chromatic dispersion: the accumulated dispersion [s/m]
         """
+        freq = self.params.ref_frequency if freq is None else freq
         beta2 = self.params.beta2
         beta3 = self.params.beta3
         ref_f = self.params.ref_frequency
@@ -398,147 +448,103 @@ class Fiber(_Node):
         """differential group delay (PMD) [s]"""
         return self.params.pmd_coef * sqrt(self.params.length)
 
-    def _gn_analytic(self, carrier, *carriers):
-        r"""Computes the nonlinear interference power on a single carrier.
-        The method uses eq. 120 from `arXiv:1209.0394 <https://arxiv.org/abs/1209.0394>`__.
-
-        :param carrier: the signal under analysis
-        :param \*carriers: the full WDM comb
-        :return: carrier_nli: the amount of nonlinear interference in W on the under analysis
+    def propagate(self, spectral_info: SpectralInformation):
+        """Modifies the spectral information computing the attenuation, the non-linear interference generation,
+        the CD and PMD accumulation.
         """
+        # apply the attenuation due to the input connector loss
+        attenuation_in_db = self.params.con_in + self.params.att_in
+        spectral_info.apply_attenuation_db(attenuation_in_db)
 
-        g_nli = 0
-        for interfering_carrier in carriers:
-            psi = _psi(carrier, interfering_carrier, beta2=self.params.beta2,
-                       asymptotic_length=self.params.asymptotic_length)
-            g_nli += (interfering_carrier.power.signal / interfering_carrier.baud_rate)**2 \
-                * (carrier.power.signal / carrier.baud_rate) * psi
+        # inter channels Raman effect
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
 
-        g_nli *= (16 / 27) * (self.params.gamma * self.params.effective_length)**2 \
-            / (2 * pi * abs(self.params.beta2) * self.params.asymptotic_length)
+        # NLI noise evaluated at the fiber input
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
 
-        carrier_nli = carrier.baud_rate * g_nli
-        return carrier_nli
+        # chromatic dispersion and pmd variations
+        spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
-    def propagate(self, *carriers):
-        r"""Generator that computes the fiber propagation: attenuation, non-linear interference generation, CD
-        accumulation and PMD accumulation.
+        # apply the attenuation due to the fiber losses
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:, -1]
+        spectral_info.apply_attenuation_lin(attenuation_fiber)
 
-        :param: \*carriers: the channels at the input of the fiber
-        :yield: carrier: the next channel at the output of the fiber
-        """
+        # apply the attenuation due to the output connector loss
+        attenuation_out_db = self.params.con_out
+        spectral_info.apply_attenuation_db(attenuation_out_db)
 
-        # apply connector_att_in on all carriers before computing gn analytics  premiere partie pas bonne
-        attenuation = db2lin(self.params.con_in + self.params.att_in)
-
-        chan = []
-        for carrier in carriers:
-            pwr = carrier.power
-            pwr = pwr._replace(signal=pwr.signal / attenuation,
-                               nli=pwr.nli / attenuation,
-                               ase=pwr.ase / attenuation)
-            carrier = carrier._replace(power=pwr)
-            chan.append(carrier)
-
-        carriers = tuple(f for f in chan)
-
-        # propagate in the fiber and apply attenuation out
-        attenuation = db2lin(self.params.con_out)
-        for carrier in carriers:
-            pwr = carrier.power
-            carrier_nli = self._gn_analytic(carrier, *carriers)
-            pwr = pwr._replace(signal=pwr.signal / self.params.lin_attenuation / attenuation,
-                               nli=(pwr.nli + carrier_nli) / self.params.lin_attenuation / attenuation,
-                               ase=pwr.ase / self.params.lin_attenuation / attenuation)
-            chromatic_dispersion = carrier.chromatic_dispersion + self.chromatic_dispersion(carrier.frequency)
-            pmd = sqrt(carrier.pmd**2 + self.pmd**2)
-            yield carrier._replace(power=pwr, chromatic_dispersion=chromatic_dispersion, pmd=pmd)
-
-    def update_pref(self, pref):
-        self.pch_out_db = round(pref.p_spani - self.loss, 2)
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.pch_out_db)
+    def update_pref(self, spectral_info):
+        # in case of Raman, the resulting loss of the fiber is not equivalent to self.loss
+        # because of Raman gain. In order to correctly update pref, we need the resulting loss:
+        # power_out - power_in. We use the total signal power (sum on all channels) to compute
+        # this loss, because pref is a noiseless reference.
+        loss = round(lin2db(self._psig_in / sum(spectral_info.signal)), 2)
+        self.pch_out_db = spectral_info.pref.p_spani - loss
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
+                                                         p_spani=self.pch_out_db)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(*spectral_info.carriers))
-        pref = self.update_pref(spectral_info.pref)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        # _psig_in records the total signal power of the spectral information before propagation.
+        self._psig_in = sum(spectral_info.signal)
+        self.propagate(spectral_info)
+        self.update_pref(spectral_info)
+        return spectral_info
 
 
 class RamanFiber(Fiber):
     def __init__(self, *args, params=None, **kwargs):
         super().__init__(*args, params=params, **kwargs)
-        if self.operational and 'raman_pumps' in self.operational:
-            self.raman_pumps = tuple(PumpParams(p['power'], p['frequency'], p['propagation_direction'])
-                                     for p in self.operational['raman_pumps'])
-        else:
-            self.raman_pumps = None
-        self.raman_solver = RamanSolver(self)
+        if not self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without operational parameters')
+
+        if 'raman_pumps' not in self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without raman pumps description in operational')
+
+        if 'temperature' not in self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without temperature in operational')
+
+        pump_loss = db2lin(self.params.con_out)
+        self.raman_pumps = tuple(PumpParams(p['power'] / pump_loss, p['frequency'], p['propagation_direction'])
+                                 for p in self.operational['raman_pumps'])
+        self.temperature = self.operational['temperature']
 
     @property
     def to_json(self):
         return dict(super().to_json, operational=self.operational)
 
-    def update_pref(self, pref, *carriers):
-        pch_out_db = lin2db(mean([carrier.power.signal for carrier in carriers])) + 30
-        self.pch_out_db = round(pch_out_db, 2)
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.pch_out_db)
+    def propagate(self, spectral_info: SpectralInformation):
+        """Modifies the spectral information computing the attenuation, the non-linear interference generation,
+        the CD and PMD accumulation.
+        """
+        # apply the attenuation due to the input connector loss
+        attenuation_in_db = self.params.con_in + self.params.att_in
+        spectral_info.apply_attenuation_db(attenuation_in_db)
 
-    def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(*spectral_info.carriers))
-        pref = self.update_pref(spectral_info.pref, *carriers)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        # Raman pumps and inter channel Raman effect
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
+        spontaneous_raman_scattering = \
+            RamanSolver.calculate_spontaneous_raman_scattering(spectral_info, stimulated_raman_scattering, self)
 
-    def propagate(self, *carriers):
-        for propagated_carrier in propagate_raman_fiber(self, *carriers):
-            chromatic_dispersion = propagated_carrier.chromatic_dispersion + \
-                                   self.chromatic_dispersion(propagated_carrier.frequency)
-            pmd = sqrt(propagated_carrier.pmd**2 + self.pmd**2)
-            propagated_carrier = propagated_carrier._replace(chromatic_dispersion=chromatic_dispersion, pmd=pmd)
-            yield propagated_carrier
+        # nli and ase noise evaluated at the fiber input
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
+        spectral_info.ase += spontaneous_raman_scattering
 
+        # chromatic dispersion and pmd variations
+        spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
-class EdfaParams:
-    def __init__(self, **params):
-        self.update_params(params)
-        if params == {}:
-            self.type_variety = ''
-            self.type_def = ''
-            # self.gain_flatmax = 0
-            # self.gain_min = 0
-            # self.p_max = 0
-            # self.nf_model = None
-            # self.nf_fit_coeff = None
-            # self.nf_ripple = None
-            # self.dgt = None
-            # self.gain_ripple = None
-            # self.out_voa_auto = False
-            # self.allowed_for_design = None
+        # apply the attenuation due to the fiber losses
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:spectral_info.number_of_channels, -1]
 
-    def update_params(self, kwargs):
-        for k, v in kwargs.items():
-            setattr(self, k, self.update_params(**v) if isinstance(v, dict) else v)
+        spectral_info.apply_attenuation_lin(attenuation_fiber)
 
-
-class EdfaOperational:
-    default_values = {
-        'gain_target': None,
-        'delta_p': None,
-        'out_voa': None,
-        'tilt_target': 0
-    }
-
-    def __init__(self, **operational):
-        self.update_attr(operational)
-
-    def update_attr(self, kwargs):
-        clean_kwargs = {k: v for k, v in kwargs.items() if v != ''}
-        for k, v in self.default_values.items():
-            setattr(self, k, clean_kwargs.get(k, v))
-
-    def __repr__(self):
-        return (f'{type(self).__name__}('
-                f'gain_target={self.gain_target!r}, '
-                f'tilt_target={self.tilt_target!r})')
+        # apply the attenuation due to the output connector loss
+        attenuation_out_db = self.params.con_out
+        spectral_info.apply_attenuation_db(attenuation_out_db)
 
 
 class Edfa(_Node):
@@ -548,12 +554,7 @@ class Edfa(_Node):
         if operational is None:
             operational = {}
         self.variety_list = kwargs.pop('variety_list', None)
-        super().__init__(
-            *args,
-            params=EdfaParams(**params),
-            operational=EdfaOperational(**operational),
-            **kwargs
-        )
+        super().__init__(*args, params=EdfaParams(**params), operational=EdfaOperational(**operational), **kwargs)
         self.interpol_dgt = None  # interpolated dynamic gain tilt
         self.interpol_gain_ripple = None  # gain ripple
         self.interpol_nf_ripple = None  # nf_ripple
@@ -579,7 +580,7 @@ class Edfa(_Node):
                 'type': type(self).__name__,
                 'type_variety': self.params.type_variety,
                 'operational': {
-                    'gain_target': self.effective_gain,
+                    'gain_target': round(self.effective_gain, 6),
                     'delta_p': self.delta_p,
                     'tilt_target': self.tilt_target,
                     'out_voa': self.out_voa
@@ -619,26 +620,33 @@ class Edfa(_Node):
                           f'  effective pch (dBm):    {self.effective_pch_out_db:.2f}',
                           f'  output VOA (dB):        {self.out_voa:.2f}'])
 
-    def interpol_params(self, frequencies, pin, baud_rates, pref):
+    def interpol_params(self, spectral_info):
         """interpolate SI channel frequencies with the edfa dgt and gain_ripple frquencies from JSON
+        :param spectral_info: instance of gnpy.core.info.SpectralInformation
+        :return: None
         """
         # TODO|jla: read amplifier actual frequencies from additional params in json
-        self.channel_freq = frequencies
 
+        self.channel_freq = spectral_info.frequency
         amplifier_freq = arrange_frequencies(len(self.params.dgt), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_dgt = interp(self.channel_freq, amplifier_freq, self.params.dgt)
+        self.interpol_dgt = interp(spectral_info.frequency, amplifier_freq, self.params.dgt)
 
         amplifier_freq = arrange_frequencies(len(self.params.gain_ripple), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_gain_ripple = interp(self.channel_freq, amplifier_freq, self.params.gain_ripple)
+        self.interpol_gain_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.gain_ripple)
 
         amplifier_freq = arrange_frequencies(len(self.params.nf_ripple), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_nf_ripple = interp(self.channel_freq, amplifier_freq, self.params.nf_ripple)
+        self.interpol_nf_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.nf_ripple)
 
-        self.nch = frequencies.size
+        self.nch = spectral_info.number_of_channels
+        pin = spectral_info.signal + spectral_info.ase + spectral_info.nli
         self.pin_db = lin2db(sum(pin * 1e3))
+        # The following should be changed when we have the new spectral information including slot widths.
+        # For now, with homogeneous spectrum, we can calculate it as the difference between neighbouring channels.
+        self.slot_width = self.channel_freq[1] - self.channel_freq[0]
 
         """in power mode: delta_p is defined and can be used to calculate the power target
         This power target is used calculate the amplifier gain"""
+        pref = spectral_info.pref
         if self.delta_p is not None:
             self.target_pch_out_db = round(self.delta_p + pref.p_span0, 2)
             self.effective_gain = self.target_pch_out_db - pref.p_spani
@@ -656,7 +664,7 @@ class Edfa(_Node):
         self.nf = self._calc_nf()
         self.gprofile = self._gain_profile(pin)
 
-        pout = (pin + self.noise_profile(baud_rates)) * db2lin(self.gprofile)
+        pout = (pin + self.noise_profile(spectral_info)) * db2lin(self.gprofile)
         self.pout_db = lin2db(sum(pout * 1e3))
         # ase & nli are only calculated in signal bandwidth
         #    pout_db is not the absolute full output power (negligible if sufficient channels)
@@ -673,13 +681,17 @@ class Edfa(_Node):
         elif type_def == 'fixed_gain':
             nf_avg = nf_model.nf0
         elif type_def == 'openroadm':
-            pin_ch = self.pin_db - lin2db(self.nch)
-            # model OSNR = f(Pin)
-            nf_avg = pin_ch - polyval(nf_model.nf_coef, pin_ch) + 58
+            # OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
+            # scale it to 50 GHz based on actual slot width.
+            pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
+            # model OSNR = f(Pin per 50 GHz channel)
+            nf_avg = pin_ch_50GHz - polyval(nf_model.nf_coef, pin_ch_50GHz) + 58
         elif type_def == 'openroadm_preamp':
-            pin_ch = self.pin_db - lin2db(self.nch)
-            # model OSNR = f(Pin)
-            nf_avg = pin_ch - min((4 * pin_ch + 275) / 7, 33) + 58
+            # OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
+            # scale it to 50 GHz based on actual slot width.
+            pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
+            # model OSNR = f(Pin per 50 GHz channel)
+            nf_avg = pin_ch_50GHz - min((4 * pin_ch_50GHz + 275) / 7, 33) + 58
         elif type_def == 'openroadm_booster':
             # model a zero-noise amp with "infinitely negative" (in dB) NF
             nf_avg = float('-inf')
@@ -725,13 +737,8 @@ class Edfa(_Node):
         else:
             return self.interpol_nf_ripple + nf_avg  # input VOA = 1 for 1 NF degradation
 
-    def noise_profile(self, df):
-        """noise_profile(bw) computes amplifier ASE (W) in signal bandwidth (Hz)
-
-        Noise is calculated at amplifier input
-
-        :bw: signal bandwidth = baud rate in Hz
-        :type bw: float
+    def noise_profile(self, spectral_info: SpectralInformation):
+        """Computes amplifier ASE noise integrated over the signal bandwidth. This is calculated at amplifier input.
 
         :return: the asepower in W in the signal bandwidth bw for 96 channels
         :return type: numpy array of float
@@ -767,7 +774,7 @@ class Edfa(_Node):
         quoting power spectral density in the same BW for both signal and ASE,
         e.g. 12.5GHz."""
 
-        ase = h * df * self.channel_freq * db2lin(self.nf)  # W
+        ase = h * spectral_info.baud_rate * spectral_info.frequency * db2lin(self.nf)  # W
         return ase  # in W at amplifier input
 
     def _gain_profile(self, pin, err_tolerance=1.0e-11, simple_opt=True):
@@ -873,30 +880,24 @@ class Edfa(_Node):
 
         return g1st - voa + array(self.interpol_dgt) * dgts3
 
-    def propagate(self, pref, *carriers):
+    def propagate(self, spectral_info):
         """add ASE noise to the propagating carriers of :class:`.info.SpectralInformation`"""
-        pin = array([c.power.signal + c.power.nli + c.power.ase for c in carriers])  # pin in W
-        freq = array([c.frequency for c in carriers])
-        brate = array([c.baud_rate for c in carriers])
         # interpolate the amplifier vectors with the carriers freq, calculate nf & gain profile
-        self.interpol_params(freq, pin, brate, pref)
+        self.interpol_params(spectral_info)
 
-        gains = db2lin(self.gprofile)
-        carrier_ases = self.noise_profile(brate)
-        att = db2lin(self.out_voa)
+        ase = self.noise_profile(spectral_info)
+        spectral_info.ase += ase
 
-        for gain, carrier_ase, carrier in zip(gains, carrier_ases, carriers):
-            pwr = carrier.power
-            pwr = pwr._replace(signal=pwr.signal * gain / att,
-                               nli=pwr.nli * gain / att,
-                               ase=(pwr.ase + carrier_ase) * gain / att)
-            yield carrier._replace(power=pwr)
+        spectral_info.apply_gain_db(self.gprofile - self.out_voa)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
+        spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
 
-    def update_pref(self, pref):
-        return pref._replace(p_span0=pref.p_span0,
-                             p_spani=pref.p_spani + self.effective_gain - self.out_voa)
+    def update_pref(self, spectral_info):
+        spectral_info.pref = \
+            spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
+                                        p_spani=spectral_info.pref.p_spani + self.effective_gain - self.out_voa)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(spectral_info.pref, *spectral_info.carriers))
-        pref = self.update_pref(spectral_info.pref)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        self.propagate(spectral_info)
+        self.update_pref(spectral_info)
+        return spectral_info

gnpy/core/equipment.py

@@ -35,6 +35,7 @@ def trx_mode_params(equipment, trx_type_variety='', trx_mode='', error_message=F
             mode_params = {"format": "undetermined",
                            "baud_rate": None,
                            "OSNR": None,
+                           "penalties": None,
                            "bit_rate": None,
                            "roll_off": None,
                            "tx_osnr": None,
@@ -59,6 +60,7 @@ def trx_mode_params(equipment, trx_type_variety='', trx_mode='', error_message=F
             trx_params['baud_rate'] = default_si_data.baud_rate
             trx_params['spacing'] = default_si_data.spacing
             trx_params['OSNR'] = None
+            trx_params['penalties'] = {}
             trx_params['bit_rate'] = None
             trx_params['cost'] = None
             trx_params['roll_off'] = default_si_data.roll_off

gnpy/core/info.py

@@ -8,24 +8,36 @@ gnpy.core.info
 This module contains classes for modelling :class:`SpectralInformation`.
 """
 
+from __future__ import annotations
 from collections import namedtuple
-from gnpy.core.utils import automatic_nch, lin2db
+from collections.abc import Iterable
+from typing import Union
+from numpy import argsort, mean, array, append, ones, ceil, any, zeros, outer, full, ndarray, asarray
+
+from gnpy.core.utils import automatic_nch, lin2db, db2lin
+from gnpy.core.exceptions import SpectrumError
+
+DEFAULT_SLOT_WIDTH_STEP = 12.5e9  # Hz
+"""Channels with unspecified slot width will have their slot width evaluated as the baud rate rounded up to the minimum
+multiple of the DEFAULT_SLOT_WIDTH_STEP (the baud rate is extended including the roll off in this evaluation)"""
 
 
 class Power(namedtuple('Power', 'signal nli ase')):
     """carriers power in W"""
 
 
-class Channel(namedtuple('Channel', 'channel_number frequency baud_rate roll_off power chromatic_dispersion pmd')):
+class Channel(namedtuple('Channel',
+                         'channel_number frequency baud_rate slot_width roll_off power chromatic_dispersion pmd pdl')):
     """ Class containing the parameters of a WDM signal.
-
         :param channel_number: channel number in the WDM grid
         :param frequency: central frequency of the signal (Hz)
         :param baud_rate: the symbol rate of the signal (Baud)
+        :param slot_width: the slot width (Hz)
         :param roll_off: the roll off of the signal. It is a pure number between 0 and 1
         :param power (gnpy.core.info.Power): power of signal, ASE noise and NLI (W)
         :param chromatic_dispersion: chromatic dispersion (s/m)
         :param pmd: polarization mode dispersion (s)
+        :param pdl: polarization dependent loss (dB)
     """
 
 
@@ -36,21 +48,217 @@ class Pref(namedtuple('Pref', 'p_span0, p_spani, neq_ch ')):
     neq_ch: equivalent channel count in dB"""
 
 
-class SpectralInformation(namedtuple('SpectralInformation', 'pref carriers')):
+class SpectralInformation(object):
+    """ Class containing the parameters of the entire WDM comb."""
 
-    def __new__(cls, pref, carriers):
-        return super().__new__(cls, pref, carriers)
+    def __init__(self, frequency: array, baud_rate: array, slot_width: array, signal: array, nli: array, ase: array,
+                 roll_off: array, chromatic_dispersion: array, pmd: array, pdl: array):
+        indices = argsort(frequency)
+        self._frequency = frequency[indices]
+        self._df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
+        self._number_of_channels = len(self._frequency)
+        self._channel_number = [*range(1, self._number_of_channels + 1)]
+        self._slot_width = slot_width[indices]
+        self._baud_rate = baud_rate[indices]
+        overlap = self._frequency[:-1] + self._slot_width[:-1] / 2 > self._frequency[1:] - self._slot_width[1:] / 2
+        if any(overlap):
+            overlap = [pair for pair in zip(overlap * self._channel_number[:-1], overlap * self._channel_number[1:])
+                       if pair != (0, 0)]
+            raise SpectrumError(f'Spectrum required slot widths larger than the frequency spectral distances '
+                                f'between channels: {overlap}.')
+        exceed = self._baud_rate > self._slot_width
+        if any(exceed):
+            raise SpectrumError(f'Spectrum baud rate, including the roll off, larger than the slot width for channels: '
+                                f'{[ch for ch in exceed * self._channel_number if ch]}.')
+        self._signal = signal[indices]
+        self._nli = nli[indices]
+        self._ase = ase[indices]
+        self._roll_off = roll_off[indices]
+        self._chromatic_dispersion = chromatic_dispersion[indices]
+        self._pmd = pmd[indices]
+        self._pdl = pdl[indices]
+        pref = lin2db(mean(signal) * 1e3)
+        self._pref = Pref(pref, pref, lin2db(self._number_of_channels))
+
+    @property
+    def pref(self):
+        """Instance of gnpy.info.Pref"""
+        return self._pref
+
+    @pref.setter
+    def pref(self, pref: Pref):
+        self._pref = pref
+
+    @property
+    def frequency(self):
+        return self._frequency
+
+    @property
+    def df(self):
+        """Matrix of relative frequency distances between all channels. Positive elements in the upper right side."""
+        return self._df
+
+    @property
+    def slot_width(self):
+        return self._slot_width
+
+    @property
+    def baud_rate(self):
+        return self._baud_rate
+
+    @property
+    def number_of_channels(self):
+        return self._number_of_channels
+
+    @property
+    def powers(self):
+        powers = zip(self.signal, self.nli, self.ase)
+        return [Power(*p) for p in powers]
+
+    @property
+    def signal(self):
+        return self._signal
+
+    @signal.setter
+    def signal(self, signal):
+        self._signal = signal
+
+    @property
+    def nli(self):
+        return self._nli
+
+    @nli.setter
+    def nli(self, nli):
+        self._nli = nli
+
+    @property
+    def ase(self):
+        return self._ase
+
+    @ase.setter
+    def ase(self, ase):
+        self._ase = ase
+
+    @property
+    def roll_off(self):
+        return self._roll_off
+
+    @property
+    def chromatic_dispersion(self):
+        return self._chromatic_dispersion
+
+    @chromatic_dispersion.setter
+    def chromatic_dispersion(self, chromatic_dispersion):
+        self._chromatic_dispersion = chromatic_dispersion
+
+    @property
+    def pmd(self):
+        return self._pmd
+
+    @pmd.setter
+    def pmd(self, pmd):
+        self._pmd = pmd
+
+    @property
+    def pdl(self):
+        return self._pdl
+
+    @pdl.setter
+    def pdl(self, pdl):
+        self._pdl = pdl
+
+    @property
+    def channel_number(self):
+        return self._channel_number
+
+    @property
+    def carriers(self):
+        entries = zip(self.channel_number, self.frequency, self.baud_rate, self.slot_width,
+                      self.roll_off, self.powers, self.chromatic_dispersion, self.pmd, self.pdl)
+        return [Channel(*entry) for entry in entries]
+
+    def apply_attenuation_lin(self, attenuation_lin):
+        self.signal *= attenuation_lin
+        self.nli *= attenuation_lin
+        self.ase *= attenuation_lin
+
+    def apply_attenuation_db(self, attenuation_db):
+        attenuation_lin = 1 / db2lin(attenuation_db)
+        self.apply_attenuation_lin(attenuation_lin)
+
+    def apply_gain_lin(self, gain_lin):
+        self.signal *= gain_lin
+        self.nli *= gain_lin
+        self.ase *= gain_lin
+
+    def apply_gain_db(self, gain_db):
+        gain_lin = db2lin(gain_db)
+        self.apply_gain_lin(gain_lin)
+
+    def __add__(self, other: SpectralInformation):
+        try:
+            return SpectralInformation(frequency=append(self.frequency, other.frequency),
+                                       slot_width=append(self.slot_width, other.slot_width),
+                                       signal=append(self.signal, other.signal), nli=append(self.nli, other.nli),
+                                       ase=append(self.ase, other.ase),
+                                       baud_rate=append(self.baud_rate, other.baud_rate),
+                                       roll_off=append(self.roll_off, other.roll_off),
+                                       chromatic_dispersion=append(self.chromatic_dispersion,
+                                                                   other.chromatic_dispersion),
+                                       pmd=append(self.pmd, other.pmd),
+                                       pdl=append(self.pdl, other.pdl))
+        except SpectrumError:
+            raise SpectrumError('Spectra cannot be summed: channels overlapping.')
+
+    def _replace(self, carriers, pref):
+        self.chromatic_dispersion = array([c.chromatic_dispersion for c in carriers])
+        self.pmd = array([c.pmd for c in carriers])
+        self.pdl = array([c.pdl for c in carriers])
+        self.signal = array([c.power.signal for c in carriers])
+        self.nli = array([c.power.nli for c in carriers])
+        self.ase = array([c.power.ase for c in carriers])
+        self.pref = pref
+        return self
+
+
+def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, int, float],
+                                          signal: Union[int, float, ndarray, Iterable],
+                                          baud_rate: Union[int, float, ndarray, Iterable],
+                                          slot_width: Union[int, float, ndarray, Iterable] = None,
+                                          roll_off: Union[int, float, ndarray, Iterable] = 0.,
+                                          chromatic_dispersion: Union[int, float, ndarray, Iterable] = 0.,
+                                          pmd: Union[int, float, ndarray, Iterable] = 0.,
+                                          pdl: Union[int, float, ndarray, Iterable] = 0.):
+    """This is just a wrapper around the SpectralInformation.__init__() that simplifies the creation of
+    a non-uniform spectral information with NLI and ASE powers set to zero."""
+    frequency = asarray(frequency)
+    number_of_channels = frequency.size
+    try:
+        signal = full(number_of_channels, signal)
+        baud_rate = full(number_of_channels, baud_rate)
+        roll_off = full(number_of_channels, roll_off)
+        slot_width = full(number_of_channels, slot_width) if slot_width is not None else \
+            ceil((1 + roll_off) * baud_rate / DEFAULT_SLOT_WIDTH_STEP) * DEFAULT_SLOT_WIDTH_STEP
+        chromatic_dispersion = full(number_of_channels, chromatic_dispersion)
+        pmd = full(number_of_channels, pmd)
+        pdl = full(number_of_channels, pdl)
+        nli = zeros(number_of_channels)
+        ase = zeros(number_of_channels)
+        return SpectralInformation(frequency=frequency, slot_width=slot_width,
+                                   signal=signal, nli=nli, ase=ase,
+                                   baud_rate=baud_rate, roll_off=roll_off,
+                                   chromatic_dispersion=chromatic_dispersion,
+                                   pmd=pmd, pdl=pdl)
+    except ValueError as e:
+        if 'could not broadcast' in str(e):
+            raise SpectrumError('Dimension mismatch in input fields.')
+        else:
+            raise
 
 
 def create_input_spectral_information(f_min, f_max, roll_off, baud_rate, power, spacing):
-    # pref in dB : convert power lin into power in dB
-    pref = lin2db(power * 1e3)
+    """ Creates a fixed slot width spectral information with flat power """
     nb_channel = automatic_nch(f_min, f_max, spacing)
-    si = SpectralInformation(
-        pref=Pref(pref, pref, lin2db(nb_channel)),
-        carriers=[
-            Channel(f, (f_min + spacing * f),
-                    baud_rate, roll_off, Power(power, 0, 0), 0, 0) for f in range(1, nb_channel + 1)
-        ]
-    )
-    return si
+    frequency = [(f_min + spacing * i) for i in range(1, nb_channel + 1)]
+    return create_arbitrary_spectral_information(frequency, slot_width=spacing, signal=power, baud_rate=baud_rate,
+                                                 roll_off=roll_off)

gnpy/core/network.py

@@ -27,6 +27,7 @@ def edfa_nf(gain_target, variety_type, equipment):
     )
     amp.pin_db = 0
     amp.nch = 88
+    amp.slot_width = 50e9
     return amp._calc_nf(True)
 
 
@@ -282,7 +283,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
 
                 if isinstance(prev_node, elements.Fiber):
                     max_fiber_lineic_loss_for_raman = \
-                        equipment['Span']['default'].max_fiber_lineic_loss_for_raman
+                        equipment['Span']['default'].max_fiber_lineic_loss_for_raman * 1e-3  # dB/m
                     raman_allowed = prev_node.params.loss_coef < max_fiber_lineic_loss_for_raman
                 else:
                     raman_allowed = False
@@ -303,9 +304,14 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
                     node.params.update_params(extra_params.__dict__)
                     dp += power_reduction
                     gain_target += power_reduction
-                elif node.params.raman and not raman_allowed:
-                    print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n but fiber lineic loss is above threshold\n')
                 else:
+                    if node.params.raman and not raman_allowed:
+                        if isinstance(prev_node, elements.Fiber):
+                            print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n '
+                                  'but fiber lineic loss is above threshold\n')
+                        else:
+                            print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n '
+                                  'but previous node is not a fiber\n')
                     # if variety is imposed by user, and if the gain_target (computed or imposed) is also above
                     # variety max gain + extended range, then warn that gain > max_gain + extended range
                     if gain_target - equipment['Edfa'][node.params.type_variety].gain_flatmax - \
@@ -521,7 +527,6 @@ def build_network(network, equipment, pref_ch_db, pref_total_db, no_insert_edfas
     # set roadm loss for gain_mode before to build network
     fibers = [f for f in network.nodes() if isinstance(f, elements.Fiber)]
     add_connector_loss(network, fibers, default_span_data.con_in, default_span_data.con_out, default_span_data.EOL)
-    add_fiber_padding(network, fibers, default_span_data.padding)
     # don't group split fiber and add amp in the same loop
     # =>for code clarity (at the expense of speed):
 
@@ -539,6 +544,8 @@ def build_network(network, equipment, pref_ch_db, pref_total_db, no_insert_edfas
         for fiber in fibers:
             add_inline_amplifier(network, fiber)
 
+    add_fiber_padding(network, fibers, default_span_data.padding)
+
     for roadm in roadms:
         set_egress_amplifier(network, roadm, equipment, pref_ch_db, pref_total_db)
 

gnpy/core/parameters.py

@@ -9,9 +9,9 @@ This module contains all parameters to configure standard network elements.
 """
 
 from scipy.constants import c, pi
-from numpy import squeeze, log10, exp
+from numpy import asarray, array
 
-from gnpy.core.utils import db2lin, convert_length
+from gnpy.core.utils import convert_length
 from gnpy.core.exceptions import ParametersError
 
 
@@ -28,110 +28,102 @@ class Parameters:
 
 class PumpParams(Parameters):
     def __init__(self, power, frequency, propagation_direction):
-        self._power = power
-        self._frequency = frequency
-        self._propagation_direction = propagation_direction
-
-    @property
-    def power(self):
-        return self._power
-
-    @property
-    def frequency(self):
-        return self._frequency
-
-    @property
-    def propagation_direction(self):
-        return self._propagation_direction
+        self.power = power
+        self.frequency = frequency
+        self.propagation_direction = propagation_direction.lower()
 
 
 class RamanParams(Parameters):
-    def __init__(self, **kwargs):
-        self._flag_raman = kwargs['flag_raman']
-        self._space_resolution = kwargs['space_resolution'] if 'space_resolution' in kwargs else None
-        self._tolerance = kwargs['tolerance'] if 'tolerance' in kwargs else None
-
-    @property
-    def flag_raman(self):
-        return self._flag_raman
-
-    @property
-    def space_resolution(self):
-        return self._space_resolution
-
-    @property
-    def tolerance(self):
-        return self._tolerance
+    def __init__(self, flag=False, result_spatial_resolution=10e3, solver_spatial_resolution=50):
+        """ Simulation parameters used within the Raman Solver
+        :params flag: boolean for enabling/disable the evaluation of the Raman power profile in frequency and position
+        :params result_spatial_resolution: spatial resolution of the evaluated Raman power profile
+        :params solver_spatial_resolution: spatial step for the iterative solution of the first order ode
+        """
+        self.flag = flag
+        self.result_spatial_resolution = result_spatial_resolution  # [m]
+        self.solver_spatial_resolution = solver_spatial_resolution  # [m]
 
 
 class NLIParams(Parameters):
-    def __init__(self, **kwargs):
-        self._nli_method_name = kwargs['nli_method_name']
-        self._wdm_grid_size = kwargs['wdm_grid_size']
-        self._dispersion_tolerance = kwargs['dispersion_tolerance']
-        self._phase_shift_tolerance = kwargs['phase_shift_tolerance']
-        self._f_cut_resolution = None
-        self._f_pump_resolution = None
-        self._computed_channels = kwargs['computed_channels'] if 'computed_channels' in kwargs else None
-
-    @property
-    def nli_method_name(self):
-        return self._nli_method_name
-
-    @property
-    def wdm_grid_size(self):
-        return self._wdm_grid_size
-
-    @property
-    def dispersion_tolerance(self):
-        return self._dispersion_tolerance
-
-    @property
-    def phase_shift_tolerance(self):
-        return self._phase_shift_tolerance
-
-    @property
-    def f_cut_resolution(self):
-        return self._f_cut_resolution
-
-    @f_cut_resolution.setter
-    def f_cut_resolution(self, f_cut_resolution):
-        self._f_cut_resolution = f_cut_resolution
-
-    @property
-    def f_pump_resolution(self):
-        return self._f_pump_resolution
-
-    @f_pump_resolution.setter
-    def f_pump_resolution(self, f_pump_resolution):
-        self._f_pump_resolution = f_pump_resolution
-
-    @property
-    def computed_channels(self):
-        return self._computed_channels
+    def __init__(self, method='gn_model_analytic', dispersion_tolerance=1, phase_shift_tolerance=0.1,
+                 computed_channels=None):
+        """ Simulation parameters used within the Nli Solver
+        :params method: formula for NLI calculation
+        :params dispersion_tolerance: tuning parameter for ggn model solution
+        :params phase_shift_tolerance: tuning parameter for ggn model solution
+        :params computed_channels: the NLI is evaluated for these channels and extrapolated for the others
+        """
+        self.method = method.lower()
+        self.dispersion_tolerance = dispersion_tolerance
+        self.phase_shift_tolerance = phase_shift_tolerance
+        self.computed_channels = computed_channels
 
 
 class SimParams(Parameters):
-    def __init__(self, **kwargs):
-        try:
-            if 'nli_parameters' in kwargs:
-                self._nli_params = NLIParams(**kwargs['nli_parameters'])
-            else:
-                self._nli_params = None
-            if 'raman_parameters' in kwargs:
-                self._raman_params = RamanParams(**kwargs['raman_parameters'])
-            else:
-                self._raman_params = None
-        except KeyError as e:
-            raise ParametersError(f'Simulation parameters must include {e}. Configuration: {kwargs}')
+    _shared_dict = {'nli_params': NLIParams(), 'raman_params': RamanParams()}
+
+    def __init__(self):
+        if type(self) == SimParams:
+            raise NotImplementedError('Instances of SimParams cannot be generated')
+
+    @classmethod
+    def set_params(cls, sim_params):
+        cls._shared_dict['nli_params'] = NLIParams(**sim_params.get('nli_params', {}))
+        cls._shared_dict['raman_params'] = RamanParams(**sim_params.get('raman_params', {}))
+
+    @classmethod
+    def get(cls):
+        self = cls.__new__(cls)
+        return self
 
     @property
     def nli_params(self):
-        return self._nli_params
+        return self._shared_dict['nli_params']
 
     @property
     def raman_params(self):
-        return self._raman_params
+        return self._shared_dict['raman_params']
+
+
+class RoadmParams(Parameters):
+    def __init__(self, **kwargs):
+        try:
+            self.target_pch_out_db = kwargs['target_pch_out_db']
+            self.add_drop_osnr = kwargs['add_drop_osnr']
+            self.pmd = kwargs['pmd']
+            self.pdl = kwargs['pdl']
+            self.restrictions = kwargs['restrictions']
+            self.per_degree_pch_out_db = kwargs['per_degree_pch_out_db'] if 'per_degree_pch_out_db' in kwargs else {}
+        except KeyError as e:
+            raise ParametersError(f'ROADM configurations must include {e}. Configuration: {kwargs}')
+
+
+class FusedParams(Parameters):
+    def __init__(self, **kwargs):
+        self.loss = kwargs['loss'] if 'loss' in kwargs else 1
+
+
+# SSMF Raman coefficient profile normalized with respect to the effective area (Cr * A_eff)
+CR_NORM = array([
+    0., 7.802e-16, 2.4236e-15, 4.0504e-15, 5.6606e-15, 6.8973e-15, 7.802e-15, 8.4162e-15, 8.8727e-15, 9.2877e-15,
+    1.01011e-14, 1.05244e-14, 1.13295e-14, 1.2367e-14, 1.3695e-14, 1.5023e-14, 1.64091e-14, 1.81936e-14, 2.04927e-14,
+    2.28167e-14, 2.48917e-14, 2.66098e-14, 2.82615e-14, 2.98136e-14, 3.1042e-14, 3.17558e-14, 3.18803e-14, 3.17558e-14,
+    3.15566e-14, 3.11748e-14, 2.94567e-14, 3.14985e-14, 2.8552e-14, 2.43439e-14, 1.67992e-14, 9.6114e-15, 7.02180e-15,
+    5.9262e-15, 5.6938e-15, 7.055e-15, 7.4119e-15, 7.4783e-15, 6.7645e-15, 5.5361e-15, 3.6271e-15, 2.7224e-15,
+    2.4568e-15, 2.1995e-15, 2.1331e-15, 2.3323e-15, 2.5564e-15, 3.0461e-15, 4.8555e-15, 5.5029e-15, 5.2788e-15,
+    4.565e-15, 3.3698e-15, 2.2991e-15, 2.0086e-15, 1.5521e-15, 1.328e-15, 1.162e-15, 9.379e-16, 8.715e-16, 8.134e-16,
+    8.134e-16, 9.379e-16, 1.3612e-15, 1.6185e-15, 1.9754e-15, 1.8758e-15, 1.6849e-15, 1.2284e-15, 9.047e-16, 8.134e-16,
+    8.715e-16, 9.711e-16, 1.0375e-15, 1.0043e-15, 9.047e-16, 8.134e-16, 6.806e-16, 5.478e-16, 3.901e-16, 2.241e-16,
+    1.577e-16, 9.96e-17, 3.32e-17, 1.66e-17, 8.3e-18])
+
+# Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape.
+FREQ_OFFSET = array([
+    0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.,
+    12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75, 19.,
+    19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5, 29., 29.5,
+    30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5, 39., 39.5, 40.,
+    40.5, 41., 41.5, 42.]) * 1e12
 
 
 class FiberParams(Parameters):
@@ -139,45 +131,50 @@ class FiberParams(Parameters):
         try:
             self._length = convert_length(kwargs['length'], kwargs['length_units'])
             # fixed attenuator for padding
-            self._att_in = kwargs['att_in'] if 'att_in' in kwargs else 0
+            self._att_in = kwargs.get('att_in', 0)
             # if not defined in the network json connector loss in/out
             # the None value will be updated in network.py[build_network]
             # with default values from eqpt_config.json[Spans]
-            self._con_in = kwargs['con_in'] if 'con_in' in kwargs else None
-            self._con_out = kwargs['con_out'] if 'con_out' in kwargs else None
+            self._con_in = kwargs.get('con_in')
+            self._con_out = kwargs.get('con_out')
             if 'ref_wavelength' in kwargs:
                 self._ref_wavelength = kwargs['ref_wavelength']
-                self._ref_frequency = c / self.ref_wavelength
+                self._ref_frequency = c / self._ref_wavelength
             elif 'ref_frequency' in kwargs:
                 self._ref_frequency = kwargs['ref_frequency']
-                self._ref_wavelength = c / self.ref_frequency
+                self._ref_wavelength = c / self._ref_frequency
             else:
-                self._ref_wavelength = 1550e-9
-                self._ref_frequency = c / self.ref_wavelength
+                self._ref_wavelength = 1550e-9  # conventional central C band wavelength [m]
+                self._ref_frequency = c / self._ref_wavelength
             self._dispersion = kwargs['dispersion']  # s/m/m
-            self._dispersion_slope = kwargs['dispersion_slope'] if 'dispersion_slope' in kwargs else \
-                -2 * self._dispersion/self.ref_wavelength  # s/m/m/m
+            self._dispersion_slope = \
+                kwargs.get('dispersion_slope', -2 * self._dispersion / self.ref_wavelength)  # s/m/m/m
             self._beta2 = -(self.ref_wavelength ** 2) * self.dispersion / (2 * pi * c)  # 1/(m * Hz^2)
             # Eq. (3.23) in  Abramczyk, Halina. "Dispersion phenomena in optical fibers." Virtual European University
             # on Lasers. Available online: http://mitr.p.lodz.pl/evu/lectures/Abramczyk3.pdf
             # (accessed on 25 March 2018) (2005).
             self._beta3 = ((self.dispersion_slope - (4*pi*c/self.ref_wavelength**3) * self.beta2) /
                            (2*pi*c/self.ref_wavelength**2)**2)
-            self._gamma = kwargs['gamma']  # 1/W/m
+            self._effective_area = kwargs.get('effective_area')  # m^2
+            n2 = 2.6e-20  # m^2/W
+            if self._effective_area:
+                self._gamma = kwargs.get('gamma', 2 * pi * n2 / (self.ref_wavelength * self._effective_area))  # 1/W/m
+            elif 'gamma' in kwargs:
+                self._gamma = kwargs['gamma']  # 1/W/m
+                self._effective_area = 2 * pi * n2 / (self.ref_wavelength * self._gamma)  # m^2
+            else:
+                self._gamma = 0  # 1/W/m
+                self._effective_area = 83e-12  # m^2
+            default_raman_efficiency = {'cr': CR_NORM / self._effective_area, 'frequency_offset': FREQ_OFFSET}
+            self._raman_efficiency = kwargs.get('raman_efficiency', default_raman_efficiency)
             self._pmd_coef = kwargs['pmd_coef']  # s/sqrt(m)
             if type(kwargs['loss_coef']) == dict:
-                self._loss_coef = squeeze(kwargs['loss_coef']['loss_coef_power']) * 1e-3  # lineic loss dB/m
-                self._f_loss_ref = squeeze(kwargs['loss_coef']['frequency'])  # Hz
+                self._loss_coef = asarray(kwargs['loss_coef']['value']) * 1e-3  # lineic loss dB/m
+                self._f_loss_ref = asarray(kwargs['loss_coef']['frequency'])  # Hz
             else:
-                self._loss_coef = kwargs['loss_coef'] * 1e-3  # lineic loss dB/m
-                self._f_loss_ref = 193.5e12  # Hz
-            self._lin_attenuation = db2lin(self.length * self.loss_coef)
-            self._lin_loss_exp = self.loss_coef / (10 * log10(exp(1)))  # linear power exponent loss Neper/m
-            self._effective_length = (1 - exp(- self.lin_loss_exp * self.length)) / self.lin_loss_exp
-            self._asymptotic_length = 1 / self.lin_loss_exp
-            # raman parameters (not compulsory)
-            self._raman_efficiency = kwargs['raman_efficiency'] if 'raman_efficiency' in kwargs else None
-            self._pumps_loss_coef = kwargs['pumps_loss_coef'] if 'pumps_loss_coef' in kwargs else None
+                self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3  # lineic loss dB/m
+                self._f_loss_ref = asarray(self._ref_frequency)  # Hz
+            self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else []
         except KeyError as e:
             raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
 
@@ -210,6 +207,10 @@ class FiberParams(Parameters):
     def con_out(self):
         return self._con_out
 
+    @property
+    def lumped_losses(self):
+        return self._lumped_losses
+
     @con_out.setter
     def con_out(self, con_out):
         self._con_out = con_out
@@ -254,32 +255,60 @@ class FiberParams(Parameters):
     def f_loss_ref(self):
         return self._f_loss_ref
 
-    @property
-    def lin_loss_exp(self):
-        return self._lin_loss_exp
-
-    @property
-    def lin_attenuation(self):
-        return self._lin_attenuation
-
-    @property
-    def effective_length(self):
-        return self._effective_length
-
-    @property
-    def asymptotic_length(self):
-        return self._asymptotic_length
-
     @property
     def raman_efficiency(self):
         return self._raman_efficiency
 
-    @property
-    def pumps_loss_coef(self):
-        return self._pumps_loss_coef
-
     def asdict(self):
         dictionary = super().asdict()
         dictionary['loss_coef'] = self.loss_coef * 1e3
         dictionary['length_units'] = 'm'
+        if not self.lumped_losses:
+            dictionary.pop('lumped_losses')
+        if not self.raman_efficiency:
+            dictionary.pop('raman_efficiency')
         return dictionary
+
+
+class EdfaParams:
+    def __init__(self, **params):
+        self.update_params(params)
+        if params == {}:
+            self.type_variety = ''
+            self.type_def = ''
+            # self.gain_flatmax = 0
+            # self.gain_min = 0
+            # self.p_max = 0
+            # self.nf_model = None
+            # self.nf_fit_coeff = None
+            # self.nf_ripple = None
+            # self.dgt = None
+            # self.gain_ripple = None
+            # self.out_voa_auto = False
+            # self.allowed_for_design = None
+
+    def update_params(self, kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, self.update_params(**v) if isinstance(v, dict) else v)
+
+
+class EdfaOperational:
+    default_values = {
+        'gain_target': None,
+        'delta_p': None,
+        'out_voa': None,
+        'tilt_target': 0
+    }
+
+    def __init__(self, **operational):
+        self.update_attr(operational)
+
+    def update_attr(self, kwargs):
+        clean_kwargs = {k: v for k, v in kwargs.items() if v != ''}
+        for k, v in self.default_values.items():
+            setattr(self, k, clean_kwargs.get(k, v))
+
+    def __repr__(self):
+        return (f'{type(self).__name__}('
+                f'gain_target={self.gain_target!r}, '
+                f'tilt_target={self.tilt_target!r})')

gnpy/example-data/eqpt_config.json

@@ -180,51 +180,27 @@
       "Fiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
             },
             {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
             },
             {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
             }
       ],
       "RamanFiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
-            "pmd_coef": 1.265e-15,
-            "raman_efficiency": {
-              "cr":[
-                  0, 9.4E-06, 2.92E-05, 4.88E-05, 6.82E-05, 8.31E-05, 9.4E-05, 0.0001014, 0.0001069, 0.0001119,
-                  0.0001217, 0.0001268, 0.0001365, 0.000149, 0.000165, 0.000181, 0.0001977, 0.0002192, 0.0002469,
-                  0.0002749, 0.0002999, 0.0003206, 0.0003405, 0.0003592, 0.000374, 0.0003826, 0.0003841, 0.0003826,
-                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
-                  7.14E-05, 6.86E-05, 8.5E-05, 8.93E-05, 9.01E-05, 8.15E-05, 6.67E-05, 4.37E-05, 3.28E-05, 2.96E-05,
-                  2.65E-05, 2.57E-05, 2.81E-05, 3.08E-05, 3.67E-05, 5.85E-05, 6.63E-05, 6.36E-05, 5.5E-05, 4.06E-05,
-                  2.77E-05, 2.42E-05, 1.87E-05, 1.6E-05, 1.4E-05, 1.13E-05, 1.05E-05, 9.8E-06, 9.8E-06, 1.13E-05,
-                  1.64E-05, 1.95E-05, 2.38E-05, 2.26E-05, 2.03E-05, 1.48E-05, 1.09E-05, 9.8E-06, 1.05E-05, 1.17E-05,
-                  1.25E-05, 1.21E-05, 1.09E-05, 9.8E-06, 8.2E-06, 6.6E-06, 4.7E-06, 2.7E-06, 1.9E-06, 1.2E-06, 4E-07,
-                  2E-07, 1E-07
-              ],
-              "frequency_offset":[
-                0, 0.5e12, 1e12, 1.5e12, 2e12, 2.5e12, 3e12, 3.5e12, 4e12, 4.5e12, 5e12, 5.5e12, 6e12, 6.5e12, 7e12,
-                7.5e12, 8e12, 8.5e12, 9e12, 9.5e12, 10e12, 10.5e12, 11e12, 11.5e12, 12e12, 12.5e12, 12.75e12,
-                13e12, 13.25e12, 13.5e12, 14e12, 14.5e12, 14.75e12, 15e12, 15.5e12, 16e12, 16.5e12, 17e12,
-                17.5e12, 18e12, 18.25e12, 18.5e12, 18.75e12, 19e12, 19.5e12, 20e12, 20.5e12, 21e12, 21.5e12,
-                22e12, 22.5e12, 23e12, 23.5e12, 24e12, 24.5e12, 25e12, 25.5e12, 26e12, 26.5e12, 27e12, 27.5e12, 28e12,
-                28.5e12, 29e12, 29.5e12, 30e12, 30.5e12, 31e12, 31.5e12, 32e12, 32.5e12, 33e12, 33.5e12, 34e12, 34.5e12,
-                35e12, 35.5e12, 36e12, 36.5e12, 37e12, 37.5e12, 38e12, 38.5e12, 39e12, 39.5e12, 40e12, 40.5e12, 41e12,
-                41.5e12, 42e12
-              ]
-              }
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
             }
       ],
       "Span":[{
@@ -245,6 +221,7 @@
             "target_pch_out_db": -20,
             "add_drop_osnr": 38,
             "pmd": 0,
+            "pdl": 0,
             "restrictions": {
                             "preamp_variety_list":[],
                             "booster_variety_list":[]

gnpy/example-data/eqpt_config_openroadm_ver4.json

@@ -1,95 +1,80 @@
-{     "Edfa":[
-            {
+{
+    "Edfa": [
+        {
             "type_variety": "openroadm_ila_low_noise",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
-            {
+        },
+        {
             "type_variety": "openroadm_ila_standard",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
-		{
+        },
+        {
             "type_variety": "openroadm_mw_mw_preamp",
             "type_def": "openroadm_preamp",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
-		{
+        },
+        {
             "type_variety": "openroadm_mw_mw_booster",
             "type_def": "openroadm_booster",
             "gain_flatmax": 32,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            }
-      ],
-      "Fiber":[
-            {
+        }
+    ],
+    "Fiber": [
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
-            },
-            {
+        },
+        {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
-            },
-            {
+        },
+        {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
-            }
-      ],
-      "RamanFiber":[
-            {
+        }
+    ],
+    "RamanFiber": [
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
-            "pmd_coef": 1.265e-15,
-            "raman_efficiency": {
-              "cr":[
-                  0, 9.4E-06, 2.92E-05, 4.88E-05, 6.82E-05, 8.31E-05, 9.4E-05, 0.0001014, 0.0001069, 0.0001119,
-                  0.0001217, 0.0001268, 0.0001365, 0.000149, 0.000165, 0.000181, 0.0001977, 0.0002192, 0.0002469,
-                  0.0002749, 0.0002999, 0.0003206, 0.0003405, 0.0003592, 0.000374, 0.0003826, 0.0003841, 0.0003826,
-                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
-                  7.14E-05, 6.86E-05, 8.5E-05, 8.93E-05, 9.01E-05, 8.15E-05, 6.67E-05, 4.37E-05, 3.28E-05, 2.96E-05,
-                  2.65E-05, 2.57E-05, 2.81E-05, 3.08E-05, 3.67E-05, 5.85E-05, 6.63E-05, 6.36E-05, 5.5E-05, 4.06E-05,
-                  2.77E-05, 2.42E-05, 1.87E-05, 1.6E-05, 1.4E-05, 1.13E-05, 1.05E-05, 9.8E-06, 9.8E-06, 1.13E-05,
-                  1.64E-05, 1.95E-05, 2.38E-05, 2.26E-05, 2.03E-05, 1.48E-05, 1.09E-05, 9.8E-06, 1.05E-05, 1.17E-05,
-                  1.25E-05, 1.21E-05, 1.09E-05, 9.8E-06, 8.2E-06, 6.6E-06, 4.7E-06, 2.7E-06, 1.9E-06, 1.2E-06, 4E-07,
-                  2E-07, 1E-07
-              ],
-              "frequency_offset":[
-                0, 0.5e12, 1e12, 1.5e12, 2e12, 2.5e12, 3e12, 3.5e12, 4e12, 4.5e12, 5e12, 5.5e12, 6e12, 6.5e12, 7e12,
-                7.5e12, 8e12, 8.5e12, 9e12, 9.5e12, 10e12, 10.5e12, 11e12, 11.5e12, 12e12, 12.5e12, 12.75e12,
-                13e12, 13.25e12, 13.5e12, 14e12, 14.5e12, 14.75e12, 15e12, 15.5e12, 16e12, 16.5e12, 17e12,
-                17.5e12, 18e12, 18.25e12, 18.5e12, 18.75e12, 19e12, 19.5e12, 20e12, 20.5e12, 21e12, 21.5e12,
-                22e12, 22.5e12, 23e12, 23.5e12, 24e12, 24.5e12, 25e12, 25.5e12, 26e12, 26.5e12, 27e12, 27.5e12, 28e12,
-                28.5e12, 29e12, 29.5e12, 30e12, 30.5e12, 31e12, 31.5e12, 32e12, 32.5e12, 33e12, 33.5e12, 34e12, 34.5e12,
-                35e12, 35.5e12, 36e12, 36.5e12, 37e12, 37.5e12, 38e12, 38.5e12, 39e12, 39.5e12, 40e12, 40.5e12, 41e12,
-                41.5e12, 42e12
-              ]
-              }
-            }
-      ],
-      "Span":[
-            {
-            "power_mode":true,
-            "delta_power_range_db": [0,0,0],
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "Span": [
+        {
+            "power_mode": true,
+            "delta_power_range_db": [0, 0, 0],
             "max_fiber_lineic_loss_for_raman": 0.25,
             "target_extended_gain": 0,
             "max_length": 135,
@@ -99,92 +84,266 @@
             "EOL": 0,
             "con_in": 0,
             "con_out": 0
-            }
-      ],
-      "Roadm":[
-            {
+        }
+    ],
+    "Roadm": [
+        {
             "target_pch_out_db": -20,
             "add_drop_osnr": 30,
-            "pmd": 0,
+            "pmd": 3e-12,
+            "pdl": 1.5,
             "restrictions": {
-                            "preamp_variety_list":["openroadm_mw_mw_preamp"],
-                            "booster_variety_list":["openroadm_mw_mw_booster"]
-                            }            
+                "preamp_variety_list": ["openroadm_mw_mw_preamp"],
+                "booster_variety_list": ["openroadm_mw_mw_booster"]
             }
-      ],
-      "SI":[
-            {
+        }
+    ],
+    "SI": [
+        {
             "f_min": 191.3e12,
             "baud_rate": 31.57e9,
-            "f_max":196.1e12,
+            "f_max": 196.1e12,
             "spacing": 50e9,
             "power_dbm": 2,
-            "power_range_db": [0,0,1],
+            "power_range_db": [0, 0, 1],
             "roll_off": 0.15,
             "tx_osnr": 35,
             "sys_margins": 2
-            }
-      ],
-      "Transceiver":[
-		{
+        }
+    ],
+    "Transceiver": [
+        {
             "type_variety": "OpenROADM MSA ver. 4.0",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
+            },
+            "mode": [
+                {
+                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                    "baud_rate": 27.95e9,
+                    "OSNR": 17,
+                    "bit_rate": 100e9,
+                    "roll_off": null,
+                    "tx_osnr": 33,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
                         },
-            "mode":[
-                       {
-                       "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
-                       "baud_rate": 27.95e9,
-                       "OSNR": 17,
-                       "bit_rate": 100e9,
-                       "roll_off": null,
-                       "tx_osnr": 33,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       },
-					   {
-                       "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
-                       "baud_rate": 31.57e9,
-                       "OSNR": 12,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 35,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "200 Gbit/s, DP-QPSK",
-                       "baud_rate": 63.1e9,
-                       "OSNR": 17,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 36,
-                       "min_spacing": 87.5e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "300 Gbit/s, DP-8QAM",
-                       "baud_rate": 63.1e9,
-                       "OSNR": 21,
-                       "bit_rate": 300e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 36,
-                       "min_spacing": 87.5e9,
-                       "cost":1
-                       },
-					   {
-                       "format": "400 Gbit/s, DP-16QAM",
-                       "baud_rate": 63.1e9,
-                       "OSNR": 24,
-                       "bit_rate": 400e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 36,
-                       "min_spacing": 87.5e9,
-                       "cost":1
-                       }
-                   ]
-            }
-      ]
-
+                        {
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+                    "baud_rate": 31.57e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 35,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 40e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, DP-QPSK",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 17,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "300 Gbit/s, DP-8QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 21,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "400 Gbit/s, DP-16QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 24,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 12e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 20,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
 }

gnpy/example-data/eqpt_config_openroadm_ver5.json

@@ -1,105 +1,92 @@
-{     "Edfa":[
-            {
+{
+    "Edfa": [
+        {
             "type_variety": "openroadm_ila_low_noise",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
-            {
+        },
+        {
             "type_variety": "openroadm_ila_standard",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
-		{
+        },
+        {
             "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
-            {
+        },
+        {
             "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,27.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 27.99],
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
-		{
+        },
+        {
             "type_variety": "openroadm_mw_mw_booster",
             "type_def": "openroadm_booster",
             "gain_flatmax": 32,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            }
-      ],
-      "Fiber":[
-            {
+        }
+    ],
+    "Fiber": [
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
-            },
-            {
+        },
+        {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
-            },
-            {
+        },
+        {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
-            }
-      ],
-      "RamanFiber":[
-            {
+        }
+    ],
+    "RamanFiber": [
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
-            "pmd_coef": 1.265e-15,
-            "raman_efficiency": {
-              "cr":[
-                  0, 9.4E-06, 2.92E-05, 4.88E-05, 6.82E-05, 8.31E-05, 9.4E-05, 0.0001014, 0.0001069, 0.0001119,
-                  0.0001217, 0.0001268, 0.0001365, 0.000149, 0.000165, 0.000181, 0.0001977, 0.0002192, 0.0002469,
-                  0.0002749, 0.0002999, 0.0003206, 0.0003405, 0.0003592, 0.000374, 0.0003826, 0.0003841, 0.0003826,
-                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
-                  7.14E-05, 6.86E-05, 8.5E-05, 8.93E-05, 9.01E-05, 8.15E-05, 6.67E-05, 4.37E-05, 3.28E-05, 2.96E-05,
-                  2.65E-05, 2.57E-05, 2.81E-05, 3.08E-05, 3.67E-05, 5.85E-05, 6.63E-05, 6.36E-05, 5.5E-05, 4.06E-05,
-                  2.77E-05, 2.42E-05, 1.87E-05, 1.6E-05, 1.4E-05, 1.13E-05, 1.05E-05, 9.8E-06, 9.8E-06, 1.13E-05,
-                  1.64E-05, 1.95E-05, 2.38E-05, 2.26E-05, 2.03E-05, 1.48E-05, 1.09E-05, 9.8E-06, 1.05E-05, 1.17E-05,
-                  1.25E-05, 1.21E-05, 1.09E-05, 9.8E-06, 8.2E-06, 6.6E-06, 4.7E-06, 2.7E-06, 1.9E-06, 1.2E-06, 4E-07,
-                  2E-07, 1E-07
-              ],
-              "frequency_offset":[
-                0, 0.5e12, 1e12, 1.5e12, 2e12, 2.5e12, 3e12, 3.5e12, 4e12, 4.5e12, 5e12, 5.5e12, 6e12, 6.5e12, 7e12,
-                7.5e12, 8e12, 8.5e12, 9e12, 9.5e12, 10e12, 10.5e12, 11e12, 11.5e12, 12e12, 12.5e12, 12.75e12,
-                13e12, 13.25e12, 13.5e12, 14e12, 14.5e12, 14.75e12, 15e12, 15.5e12, 16e12, 16.5e12, 17e12,
-                17.5e12, 18e12, 18.25e12, 18.5e12, 18.75e12, 19e12, 19.5e12, 20e12, 20.5e12, 21e12, 21.5e12,
-                22e12, 22.5e12, 23e12, 23.5e12, 24e12, 24.5e12, 25e12, 25.5e12, 26e12, 26.5e12, 27e12, 27.5e12, 28e12,
-                28.5e12, 29e12, 29.5e12, 30e12, 30.5e12, 31e12, 31.5e12, 32e12, 32.5e12, 33e12, 33.5e12, 34e12, 34.5e12,
-                35e12, 35.5e12, 36e12, 36.5e12, 37e12, 37.5e12, 38e12, 38.5e12, 39e12, 39.5e12, 40e12, 40.5e12, 41e12,
-                41.5e12, 42e12
-              ]
-              }
-            }
-      ],
-      "Span":[
-            {
-            "power_mode":true,
-            "delta_power_range_db": [0,0,0],
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "Span": [
+        {
+            "power_mode": true,
+            "delta_power_range_db": [0, 0, 0],
             "max_fiber_lineic_loss_for_raman": 0.25,
             "target_extended_gain": 0,
             "max_length": 135,
@@ -109,102 +96,314 @@
             "EOL": 0,
             "con_in": 0,
             "con_out": 0
-            }
-      ],
-      "Roadm":[
-            {
+        }
+    ],
+    "Roadm": [
+        {
             "target_pch_out_db": -20,
             "add_drop_osnr": 33,
-            "pmd": 0,
+            "pmd": 3e-12,
+            "pdl": 1.5,
             "restrictions": {
-                            "preamp_variety_list":["openroadm_mw_mw_preamp_worstcase_ver5"],
-                            "booster_variety_list":["openroadm_mw_mw_booster"]
-                            }            
+                "preamp_variety_list": ["openroadm_mw_mw_preamp_worstcase_ver5"],
+                "booster_variety_list": ["openroadm_mw_mw_booster"]
             }
-      ],
-      "SI":[
-            {
+        }
+    ],
+    "SI": [
+        {
             "f_min": 191.3e12,
             "baud_rate": 31.57e9,
-            "f_max":196.1e12,
+            "f_max": 196.1e12,
             "spacing": 50e9,
             "power_dbm": 2,
-            "power_range_db": [0,0,1],
+            "power_range_db": [0, 0, 1],
             "roll_off": 0.15,
             "tx_osnr": 35,
             "sys_margins": 2
-            }
-      ],
-      "Transceiver":[
-		{
+        }
+    ],
+    "Transceiver": [
+        {
             "type_variety": "OpenROADM MSA ver. 5.0",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
+            },
+            "mode": [
+                {
+                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                    "baud_rate": 27.95e9,
+                    "OSNR": 17,
+                    "bit_rate": 100e9,
+                    "roll_off": null,
+                    "tx_osnr": 33,
+                    "penalties": [
                         {
-                        "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
-                        "baud_rate": 27.95e9,
-                        "OSNR": 17,
-                        "bit_rate": 100e9,
-                        "roll_off": null,
-                        "tx_osnr": 33,
-                        "min_spacing": 50e9,
-                        "cost":1
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
                         },
                         {
-                        "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
-                        "baud_rate": 31.57e9,
-                        "OSNR": 12,
-                        "bit_rate": 100e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 50e9,
-                        "cost":1
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
                         },
                         {
-                        "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
-                        "baud_rate": 31.57e9,
-                        "OSNR": 20.5,
-                        "bit_rate": 100e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 50e9,
-                        "cost":1
+                            "pmd": 10,
+                            "penalty_value": 0
                         },
                         {
-                        "format": "200 Gbit/s, DP-QPSK",
-                        "baud_rate": 63.1e9,
-                        "OSNR": 17,
-                        "bit_rate": 200e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 87.5e9,
-                        "cost":1
+                            "pmd": 30,
+                            "penalty_value": 0.5
                         },
                         {
-                        "format": "300 Gbit/s, DP-8QAM",
-                        "baud_rate": 63.1e9,
-                        "OSNR": 21,
-                        "bit_rate": 300e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 87.5e9,
-                        "cost":1
+                            "pdl": 1,
+                            "penalty_value": 0.5
                         },
                         {
-                        "format": "400 Gbit/s, DP-16QAM",
-                        "baud_rate": 63.1e9,
-                        "OSNR": 24,
-                        "bit_rate": 400e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 87.5e9,
-                        "cost":1
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
                         }
-                        ]
-            }
-      ]
-
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+                    "baud_rate": 31.57e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 48e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
+                    "baud_rate": 31.57e9,
+                    "OSNR": 20.5,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, DP-QPSK",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 17,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "300 Gbit/s, DP-8QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 21,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "400 Gbit/s, DP-16QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 24,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 12e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 20,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
 }

gnpy/example-data/raman_edfa_example_network.json

@@ -20,12 +20,12 @@
         "temperature": 283,
         "raman_pumps": [
           {
-            "power": 200e-3,
+            "power": 224.403e-3,
             "frequency": 205e12,
             "propagation_direction": "counterprop"
           },
           {
-            "power": 206e-3,
+            "power": 231.135e-3,
             "frequency": 201e12,
             "propagation_direction": "counterprop"
           }
@@ -49,6 +49,21 @@
         }
       }
     },
+    {
+      "uid": "Fused1",
+      "type": "Fused",
+      "params": {
+        "loss": 0
+      },
+      "metadata": {
+        "location": {
+          "latitude": 1.5,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    },
     {
       "uid": "Edfa1",
       "type": "Edfa",
@@ -88,6 +103,10 @@
     },
     {
       "from_node": "Span1",
+      "to_node": "Fused1"
+    },
+    {
+      "from_node": "Fused1",
       "to_node": "Edfa1"
     },
     {

gnpy/example-data/sim_params.json

@@ -1,14 +1,13 @@
 {
-  "raman_parameters": {
-    "flag_raman": true,
-    "space_resolution": 10e3,
-    "tolerance": 1e-8
+  "raman_params": {
+    "flag": true,
+    "result_spatial_resolution": 10e3,
+    "solver_spatial_resolution": 50
   },
-  "nli_parameters": {
-  	"nli_method_name": "ggn_spectrally_separated",
-  	"wdm_grid_size": 50e9,
-  	"dispersion_tolerance": 1,
-  	"phase_shift_tolerance": 0.1,
-	"computed_channels": [1, 18, 37, 56, 75] 
+  "nli_params": {
+    "method": "ggn_spectrally_separated",
+    "dispersion_tolerance": 1,
+    "phase_shift_tolerance": 0.1,
+    "computed_channels": [1, 18, 37, 56, 75]
   }
-}
+}

gnpy/tools/cli_examples.py

@@ -10,7 +10,6 @@ Common code for CLI examples
 
 import argparse
 import logging
-import os.path
 import sys
 from math import ceil
 from numpy import linspace, mean
@@ -21,7 +20,6 @@ from gnpy.core.equipment import trx_mode_params
 import gnpy.core.exceptions as exceptions
 from gnpy.core.network import build_network
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
 from gnpy.core.utils import db2lin, lin2db, automatic_nch
 from gnpy.topology.request import (ResultElement, jsontocsv, compute_path_dsjctn, requests_aggregation,
                                    BLOCKING_NOPATH, correct_json_route_list,
@@ -57,14 +55,15 @@ def load_common_data(equipment_filename, topology_filename, simulation_filename,
         if save_raw_network_filename is not None:
             save_network(network, save_raw_network_filename)
             print(f'{ansi_escapes.blue}Raw network (no optimizations) saved to {save_raw_network_filename}{ansi_escapes.reset}')
-        sim_params = SimParams(**load_json(simulation_filename)) if simulation_filename is not None else None
-        if not sim_params:
+        if not simulation_filename:
+            sim_params = {}
             if next((node for node in network if isinstance(node, RamanFiber)), None) is not None:
                 print(f'{ansi_escapes.red}Invocation error:{ansi_escapes.reset} '
                       f'RamanFiber requires passing simulation params via --sim-params')
                 sys.exit(1)
         else:
-            Simulation.set_params(sim_params)
+            sim_params = load_json(simulation_filename)
+        SimParams.set_params(sim_params)
     except exceptions.EquipmentConfigError as e:
         print(f'{ansi_escapes.red}Configuration error in the equipment library:{ansi_escapes.reset} {e}')
         sys.exit(1)

gnpy/tools/json_io.py

@@ -94,6 +94,7 @@ class Roadm(_JsonThing):
         'target_pch_out_db': -17,
         'add_drop_osnr': 100,
         'pmd': 0,
+        'pdl': 0,
         'restrictions': {
             'preamp_variety_list': [],
             'booster_variety_list': []
@@ -113,36 +114,45 @@ class Transceiver(_JsonThing):
 
     def __init__(self, **kwargs):
         self.update_attr(self.default_values, kwargs, 'Transceiver')
+        for mode_params in self.mode:
+            penalties = mode_params.get('penalties')
+            mode_params['penalties'] = {}
+            if not penalties:
+                continue
+            for impairment in ('chromatic_dispersion', 'pmd', 'pdl'):
+                imp_penalties = [p for p in penalties if impairment in p]
+                if not imp_penalties:
+                    continue
+                if all(p[impairment] > 0 for p in imp_penalties):
+                    # make sure the list of penalty values include a proper lower boundary
+                    # (we assume 0 penalty for 0 impairment)
+                    imp_penalties.insert(0, {impairment: 0, 'penalty_value': 0})
+                # make sure the list of penalty values are sorted by impairment value
+                imp_penalties.sort(key=lambda i: i[impairment])
+                # rearrange as dict of lists instead of list of dicts
+                mode_params['penalties'][impairment] = {
+                    'up_to_boundary': [p[impairment] for p in imp_penalties],
+                    'penalty_value': [p['penalty_value'] for p in imp_penalties]
+                }
 
 
 class Fiber(_JsonThing):
     default_values = {
         'type_variety': '',
         'dispersion': None,
-        'gamma': 0,
+        'effective_area': None,
         'pmd_coef': 0
     }
 
     def __init__(self, **kwargs):
-        self.update_attr(self.default_values, kwargs, 'Fiber')
+        self.update_attr(self.default_values, kwargs, self.__class__.__name__)
+        for optional in ['gamma', 'raman_efficiency']:
+            if optional in kwargs:
+                setattr(self, optional, kwargs[optional])
 
 
-class RamanFiber(_JsonThing):
-    default_values = {
-        'type_variety': '',
-        'dispersion': None,
-        'gamma': 0,
-        'pmd_coef': 0,
-        'raman_efficiency': None
-    }
-
-    def __init__(self, **kwargs):
-        self.update_attr(self.default_values, kwargs, 'RamanFiber')
-        for param in ('cr', 'frequency_offset'):
-            if param not in self.raman_efficiency:
-                raise EquipmentConfigError(f'RamanFiber.raman_efficiency: missing "{param}" parameter')
-        if self.raman_efficiency['frequency_offset'] != sorted(self.raman_efficiency['frequency_offset']):
-            raise EquipmentConfigError(f'RamanFiber.raman_efficiency.frequency_offset is not sorted')
+class RamanFiber(Fiber):
+    pass
 
 
 class Amp(_JsonThing):
@@ -162,7 +172,9 @@ class Amp(_JsonThing):
         'gain_ripple': None,
         'out_voa_auto': False,
         'allowed_for_design': False,
-        'raman': False
+        'raman': False,
+        'pmd': 0,
+        'pdl': 0
     }
 
     def __init__(self, **kwargs):
@@ -277,7 +289,7 @@ def _check_fiber_vs_raman_fiber(equipment):
     if 'RamanFiber' not in equipment:
         return
     for fiber_type in set(equipment['Fiber'].keys()) & set(equipment['RamanFiber'].keys()):
-        for attr in ('dispersion', 'dispersion-slope', 'gamma', 'pmd-coefficient'):
+        for attr in ('dispersion', 'dispersion-slope', 'effective_area', 'gamma', 'pmd-coefficient'):
             fiber = equipment['Fiber'][fiber_type]
             raman = equipment['RamanFiber'][fiber_type]
             a = getattr(fiber, attr, None)

gnpy/topology/request.py

@@ -20,7 +20,7 @@ from logging import getLogger
 from networkx import (dijkstra_path, NetworkXNoPath,
                       all_simple_paths, shortest_simple_paths)
 from networkx.utils import pairwise
-from numpy import mean
+from numpy import mean, argmin
 from gnpy.core.elements import Transceiver, Roadm
 from gnpy.core.utils import lin2db
 from gnpy.core.info import create_input_spectral_information
@@ -32,12 +32,12 @@ from math import ceil
 
 LOGGER = getLogger(__name__)
 
-RequestParams = namedtuple('RequestParams', 'request_id source destination bidir trx_type' +
-                           ' trx_mode nodes_list loose_list spacing power nb_channel f_min' +
-                           ' f_max format baud_rate OSNR bit_rate roll_off tx_osnr' +
-                           ' min_spacing cost path_bandwidth effective_freq_slot')
-DisjunctionParams = namedtuple('DisjunctionParams', 'disjunction_id relaxable link' +
-                               '_diverse node_diverse disjunctions_req')
+RequestParams = namedtuple('RequestParams', 'request_id source destination bidir trx_type'
+                           ' trx_mode nodes_list loose_list spacing power nb_channel f_min'
+                           ' f_max format baud_rate OSNR penalties bit_rate'
+                           ' roll_off tx_osnr min_spacing cost path_bandwidth effective_freq_slot')
+DisjunctionParams = namedtuple('DisjunctionParams', 'disjunction_id relaxable link_diverse'
+                               ' node_diverse disjunctions_req')
 
 
 class PathRequest:
@@ -62,6 +62,7 @@ class PathRequest:
         self.f_max = params.f_max
         self.format = params.format
         self.OSNR = params.OSNR
+        self.penalties = params.penalties
         self.bit_rate = params.bit_rate
         self.roll_off = params.roll_off
         self.tx_osnr = params.tx_osnr
@@ -348,10 +349,12 @@ def propagate(path, req, equipment):
         else:
             si = el(si)
     path[0].update_snr(req.tx_osnr)
+    path[0].calc_penalties(req.penalties)
     if any(isinstance(el, Roadm) for el in path):
         path[-1].update_snr(req.tx_osnr, equipment['Roadm']['default'].add_drop_osnr)
     else:
         path[-1].update_snr(req.tx_osnr)
+    path[-1].calc_penalties(req.penalties)
     return si
 
 
@@ -386,11 +389,13 @@ def propagate_and_optimize_mode(path, req, equipment):
             for this_mode in modes_to_explore:
                 if path[-1].snr is not None:
                     path[0].update_snr(this_mode['tx_osnr'])
+                    path[0].calc_penalties(this_mode['penalties'])
                     if any(isinstance(el, Roadm) for el in path):
                         path[-1].update_snr(this_mode['tx_osnr'], equipment['Roadm']['default'].add_drop_osnr)
                     else:
                         path[-1].update_snr(this_mode['tx_osnr'])
-                    if round(min(path[-1].snr + lin2db(this_br / (12.5e9))), 2) \
+                    path[-1].calc_penalties(this_mode['penalties'])
+                    if round(min(path[-1].snr_01nm - path[-1].total_penalty), 2) \
                             > this_mode['OSNR'] + equipment['SI']['default'].sys_margins:
                         return path, this_mode
                     else:
@@ -1107,12 +1112,16 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                 # means that at this point the mode was entered/forced by user and thus a
                 # baud_rate was defined
                 propagate(total_path, pathreq, equipment)
-                temp_snr01nm = round(mean(total_path[-1].snr+lin2db(pathreq.baud_rate/(12.5e9))), 2)
-                if temp_snr01nm < pathreq.OSNR + equipment['SI']['default'].sys_margins:
+                snr01nm_with_penalty = total_path[-1].snr_01nm - total_path[-1].total_penalty
+                min_ind = argmin(snr01nm_with_penalty)
+                if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
                     msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with' +\
-                          f' {pathreq.tsp_mode}\n\tcomputedSNR in 0.1nm = {temp_snr01nm} ' +\
-                          f'- required osnr {pathreq.OSNR} + {equipment["SI"]["default"].sys_margins} margin'
+                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
+                          f'\n\tcomputed SNR in 0.1nm = {round(total_path[-1].snr_01nm[min_ind], 2)}' +\
+                          f'\n\tCD penalty = {round(total_path[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
+                          f'\n\tPMD penalty = {round(total_path[-1].penalties["pmd"][min_ind], 2)}' +\
+                          f'\n\trequired osnr = {pathreq.OSNR}' +\
+                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     print(msg)
                     LOGGER.warning(msg)
                     pathreq.blocking_reason = 'MODE_NOT_FEASIBLE'
@@ -1152,14 +1161,16 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                 print(f'\tPath (roadsm) {[r.uid for r in rev_p if isinstance(r,Roadm)]}\n')
                 propagate(rev_p, pathreq, equipment)
                 propagated_reversed_path = rev_p
-                temp_snr01nm = round(mean(propagated_reversed_path[-1].snr +\
-                                          lin2db(pathreq.baud_rate/(12.5e9))), 2)
-                if temp_snr01nm < pathreq.OSNR + equipment['SI']['default'].sys_margins:
+                snr01nm_with_penalty = rev_p[-1].snr_01nm - rev_p[-1].total_penalty
+                min_ind = argmin(snr01nm_with_penalty)
+                if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
                     msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with' +\
-                          f' {pathreq.tsp_mode}\n' +\
-                          f'\tcomputedSNR in 0.1nm = {temp_snr01nm} -' \
-                          f' required osnr {pathreq.OSNR} + {equipment["SI"]["default"].sys_margins} margin'
+                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
+                          f'\n\tcomputed SNR in 0.1nm = {round(rev_p[-1].snr_01nm[min_ind], 2)}' +\
+                          f'\n\tCD penalty = {round(rev_p[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
+                          f'\n\tPMD penalty = {round(rev_p[-1].penalties["pmd"][min_ind], 2)}' +\
+                          f'\n\trequired osnr = {pathreq.OSNR}' +\
+                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     print(msg)
                     LOGGER.warning(msg)
                     # TODO selection of mode should also be on reversed direction !!

gnpy/topology/spectrum_assignment.py

@@ -15,7 +15,6 @@ element/oms correspondace
 
 from collections import namedtuple
 from logging import getLogger
-from math import ceil
 from gnpy.core.elements import Roadm, Transceiver
 from gnpy.core.exceptions import ServiceError, SpectrumError
 from gnpy.topology.request import compute_spectrum_slot_vs_bandwidth

requirements.txt

@@ -1,7 +1,7 @@
-matplotlib>=3.3.3,<4
-networkx>=2.5,<3
-numpy>=1.19.4,<2
-pandas>=1.1.5,<2
-pbr>=5.5.1,<6
-scipy>=1.5.4,<2
+matplotlib>=3.5.1,<4
+networkx>=2.6,<3
+numpy>=1.22.0,<2
+pandas>=1.3.5,<2
+pbr>=5.7.0,<6
+scipy>=1.7.3,<2
 xlrd>=1.2.0,<2

setup.cfg

@@ -21,6 +21,7 @@ classifier =
     Programming Language :: Python :: 3 :: Only
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
     Programming Language :: Python :: Implementation :: CPython
     Topic :: Scientific/Engineering
     Topic :: Scientific/Engineering :: Physics
@@ -39,9 +40,6 @@ warnerrors = True
 
 [files]
 packages = gnpy
-data_files =
-	examples = examples/*
-# FIXME: solve example data files
 
 [options.entry_points]
 console_scripts =

tests/compare.py

@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-from json import dump
-from pathlib import Path
-from argparse import ArgumentParser
-from collections import namedtuple
-from gnpy.tools.json_io import load_json
-
-
-class Results(namedtuple('Results', 'missing extra different expected actual')):
-    def _asdict(self):
-        return {'missing': self.missing,
-                'extra': self.extra,
-                'different': self.different}
-
-    def __str__(self):
-        rv = []
-        if self.missing:
-            rv.append('Missing: {len(self.missing)}/{len(self.expected)}')
-            rv.extend(f'\t{x}' for x in sorted(self.missing))
-        if self.extra:
-            rv.append('Extra: {len(self.extra)}/{len(self.expected)}')
-            rv.extend(f'\t{x}' for x in sorted(self.extra))
-        if self.different:
-            rv.append('Different: {len(self.different)}/{len(self.expected)}')
-            rv.extend(f'\tExpected: {x}\n\tActual:   {y}' for x, y in self.different)
-        if not self.missing and not self.extra and not self.different:
-            rv.append('All match!')
-        return '\n'.join(rv)
-
-
-class NetworksResults(namedtuple('NetworksResult', 'elements connections')):
-    def _asdict(self):
-        return {'elements': self.elements._asdict(),
-                'connections': self.connections._asdict()}
-
-    def __str__(self):
-        return '\n'.join([
-            'Elements'.center(40, '='),
-            str(self.elements),
-            'Connections'.center(40, '='),
-            str(self.connections),
-        ])
-
-
-class ServicesResults(namedtuple('ServicesResult', 'requests synchronizations')):
-    def _asdict(self):
-        return {'requests': self.requests.asdict(),
-                'synchronizations': self.synchronizations.asdict()}
-
-    def __str__(self):
-        return '\n'.join([
-            'Requests'.center(40, '='),
-            str(self.requests),
-            'Synchronizations'.center(40, '='),
-            str(self.synchronizations),
-        ])
-
-
-class PathsResults(namedtuple('PathsResults', 'paths')):
-    def _asdict(self):
-        return {'paths': self.paths.asdict()}
-
-    def __str__(self):
-        return '\n'.join([
-            'Paths'.center(40, '='),
-            str(self.paths),
-        ])
-
-
-def compare(expected, actual, key=lambda x: x):
-    expected = {key(el): el for el in expected}
-    actual = {key(el): el for el in actual}
-    missing = set(expected) - set(actual)
-    extra = set(actual) - set(expected)
-    different = [(expected[x], actual[x]) for
-                 x in set(expected) & set(actual)
-                 if expected[x] != actual[x]]
-    return Results(missing, extra, different, expected, actual)
-
-
-def compare_networks(expected, actual):
-    elements = compare(expected['elements'], actual['elements'],
-                       key=lambda el: el['uid'])
-    connections = compare(expected['connections'], actual['connections'],
-                          key=lambda el: (el['from_node'], el['to_node']))
-    return NetworksResults(elements, connections)
-
-
-def compare_services(expected, actual):
-    requests = compare(expected['path-request'], actual['path-request'],
-                       key=lambda el: el['request-id'])
-    synchronizations = compare(expected['path-request'], expected['path-request'],
-                               key=lambda el: el['request-id'])
-    if 'synchronization' in expected.keys():
-        synchronizations = compare(expected['synchronization'], actual['synchronization'],
-                                   key=lambda el: el['synchronization-id'])
-    return ServicesResults(requests, synchronizations)
-
-
-def compare_paths(expected_output, actual_output):
-    paths = compare(expected['path'], actual['path'], key=lambda el: el['path-id'])
-    return PathsResults(paths)
-
-
-COMPARISONS = {
-    'networks': compare_networks,
-    'services': compare_services,
-    'paths': compare_paths,
-}
-
-parser = ArgumentParser()
-parser.add_argument('expected_output', type=Path, metavar='FILE')
-parser.add_argument('actual_output', type=Path, metavar='FILE')
-parser.add_argument('-o', '--output', default=None)
-parser.add_argument('-c', '--comparison', choices=COMPARISONS, default='networks')
-
-
-def encode_sets(obj):
-    if isinstance(obj, set):
-        return list(obj)
-    raise TypeError(f'{obj!r} is not JSON serializable!')
-
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-    expected = load_json(args.expected_output)
-    actual = load_json(args.actual_output)
-
-    result = COMPARISONS[args.comparison](expected, actual)
-
-    if args.output:
-        with open(args.output, 'w', encoding='utf-8') as f:
-            dump(result, f, default=encode_sets, indent=2, ensure_ascii=False)
-    else:
-        print(str(result))

tests/conftest.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Copyright (C) 2020 Telecom Infra Project and GNPy contributors
+# see LICENSE.md for a list of contributors
+#
+
+import pytest
+from gnpy.core.parameters import SimParams, NLIParams, RamanParams
+
+
+@pytest.fixture
+def set_sim_params(monkeypatch):
+    monkeypatch.setattr(SimParams, '_shared_dict', {'nli_params': NLIParams(), 'raman_params': RamanParams()})

tests/data/eqpt_config.json

@@ -63,7 +63,7 @@
       "Fiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
             }
       ],
@@ -85,6 +85,7 @@
             "target_pch_out_db": -20,
             "add_drop_osnr": 38,
             "pmd": 0,
+            "pdl": 0,
             "restrictions": {
                             "preamp_variety_list":[],
                             "booster_variety_list":[]

tests/data/raman_fiber_config.json

@@ -1,224 +0,0 @@
-{
-  "uid": "Span1",
-  "params": {
-    "length": 80,
-    "loss_coef": 0.2,
-    "length_units": "km",
-    "att_in": 0,
-    "con_in": 0.5,
-    "con_out": 0.5,
-    "type_variety": "SSMF",
-    "dispersion": 0.0000167,
-    "gamma": 0.00127,
-    "pmd_coef": 1.265e-15,
-    "raman_efficiency": {
-      "cr": [
-        0,
-        0.0000094,
-        0.0000292,
-        0.0000488,
-        0.0000682,
-        0.0000831,
-        0.000094,
-        0.0001014,
-        0.0001069,
-        0.0001119,
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-        0.0001365,
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-        0.000181,
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-        0.0002192,
-        0.0002469,
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-        0.0000027,
-        0.0000019,
-        0.0000012,
-        4e-7,
-        2e-7,
-        1e-7
-      ],
-      "frequency_offset": [
-        0,
-        500000000000,
-        1000000000000,
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-        2000000000000,
-        2500000000000,
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-        40500000000000,
-        41000000000000,
-        41500000000000,
-        42000000000000
-      ]
-    }
-  },
-  "operational": {
-    "temperature": 283,
-    "raman_pumps": [
-      {
-        "power": 0.2,
-        "frequency": 205000000000000,
-        "propagation_direction": "counterprop"
-      },
-      {
-        "power": 0.206,
-        "frequency": 201000000000000,
-        "propagation_direction": "counterprop"
-      }
-    ]
-  },
-  "metadata": {
-    "location": {
-      "latitude": 1,
-      "longitude": 0,
-      "city": null,
-      "region": ""
-    }
-  }
-}

tests/data/sim_params.json

@@ -1,14 +1,13 @@
 {
-  "raman_parameters": {
-    "flag_raman": true,
-    "space_resolution": 10e3,
-    "tolerance": 1e-8
+  "raman_params": {
+    "flag": true,
+    "result_spatial_resolution": 10e3,
+    "solver_spatial_resolution": 50
   },
-  "nli_parameters": {
-    "nli_method_name": "ggn_spectrally_separated",
-    "wdm_grid_size": 50e9,
+  "nli_params": {
+    "method": "ggn_spectrally_separated",
     "dispersion_tolerance": 1,
     "phase_shift_tolerance": 0.1,
     "computed_channels": [1, 18, 37, 56, 75]
   }
-}
+}

tests/data/test_fiber_fix_expected_results.csv

@@ -0,0 +1,97 @@
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tests/data/test_fiber_flex_expected_results.csv

@@ -0,0 +1,6 @@
+signal,nli
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tests/data/test_lumped_losses_fiber_no_pumps.csv

@@ -0,0 +1,96 @@
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tests/data/test_lumped_losses_raman_fiber_config.json

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-66,9.927950010555374e-05,1.8536821682157304e-08,8.686925127148483e-08
-67,9.772837346090753e-05,1.834034757300294e-08,8.512422533827403e-08
-68,9.62041343011343e-05,1.8145993316507615e-08,8.341482250640209e-08
-69,9.470627135912848e-05,1.7953733512786736e-08,8.174028142913557e-08
-70,9.32342835979764e-05,1.776354374489084e-08,8.009985766376519e-08
-71,9.178813743816069e-05,1.757538990695628e-08,7.849321446941075e-08
-72,9.036733009485282e-05,1.7389250225057777e-08,7.691961625609573e-08
-73,8.897136946428169e-05,1.7205104136353174e-08,7.537834446343352e-08
-74,8.760740745801088e-05,1.7025340034280735e-08,7.38751341742058e-08
-75,8.626710469266231e-05,1.6847609082084475e-08,7.274492099364066e-08
-76,8.495000573672366e-05,1.6671897815367364e-08,7.16342744751107e-08
-77,8.365569697520734e-05,1.6498202874185357e-08,7.054284583689086e-08
-78,8.238374036673638e-05,1.6326516066391613e-08,6.94702656996508e-08
-79,8.11337070649851e-05,1.615683240442047e-08,6.84161724378069e-08
-80,7.990517700271111e-05,1.5989150837085435e-08,6.738021182875641e-08
-81,7.869784230919362e-05,1.5823472723367315e-08,6.63621242598539e-08
-82,7.751129541079501e-05,1.5659808141896922e-08,6.536156604375558e-08
-83,7.634513730458697e-05,1.5498175122781168e-08,6.437820072038669e-08
-84,7.530262080974513e-05,1.5364277079429572e-08,6.349909645089698e-08
-85,7.427675504203511e-05,1.523236493234819e-08,6.263403294276124e-08
-86,7.326723873728716e-05,1.510251249079146e-08,6.178275615432246e-08
-87,7.227232864620995e-05,1.4974078108462424e-08,6.094379608687809e-08
-88,7.1291797553153e-05,1.4847055996011248e-08,6.011696114034367e-08
-89,7.032542203609039e-05,1.4721440784517874e-08,5.930206291361685e-08
-90,6.937298231673965e-05,1.4597227547292096e-08,5.849891607818969e-08
-91,6.843339696762385e-05,1.447443282270653e-08,5.7706608718023645e-08
-92,6.750649045006057e-05,1.4353051811356354e-08,5.6924992809748396e-08
-93,6.65920896785063e-05,1.4233080214004659e-08,5.615392239860827e-08
-94,6.554258932109667e-05,1.407504972937325e-08,5.5268928972034444e-08
-95,6.450957734109368e-05,1.3918655180382722e-08,5.439783940506079e-08

tests/data/test_science_utils_fiber_config.json

@@ -0,0 +1,38 @@
+{
+  "uid": "Span1",
+  "params": {
+    "length": 80,
+    "loss_coef": 0.2,
+    "length_units": "km",
+    "att_in": 0,
+    "con_in": 0.5,
+    "con_out": 0.5,
+    "type_variety": "SSMF",
+    "dispersion": 0.0000167,
+    "effective_area": 83e-12,
+    "pmd_coef": 1.265e-15
+  },
+  "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 224.403e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 231.135e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+  "metadata": {
+    "location": {
+      "latitude": 1,
+      "longitude": 0,
+      "city": null,
+      "region": ""
+    }
+  }
+}

tests/invocation/openroadm-v4-Stockholm-Gothenburg

@@ -14,6 +14,7 @@ Transceiver trx_Stockholm
   OSNR ASE (signal bw, dB):  30.98
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm_Stockholm
   effective loss (dB):  22.00
   pch out (dBm):        -20.00
@@ -229,7 +230,8 @@ Transceiver trx_Gothenburg
   OSNR ASE (0.1nm, dB):      21.20
   OSNR ASE (signal bw, dB):  17.18
   CD (ps/nm):                8350.42
-  PMD (ps):                  0.89
+  PMD (ps):                  7.99
+  PDL (dB):                  3.74
 
 Transmission result for input power = 2.00 dBm:
   Final GSNR (0.1 nm): 18.90 dB

tests/invocation/openroadm-v5-Stockholm-Gothenburg

@@ -14,6 +14,7 @@ Transceiver trx_Stockholm
   OSNR ASE (signal bw, dB):  30.98
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm_Stockholm
   effective loss (dB):  22.00
   pch out (dBm):        -20.00
@@ -229,7 +230,8 @@ Transceiver trx_Gothenburg
   OSNR ASE (0.1nm, dB):      21.84
   OSNR ASE (signal bw, dB):  17.82
   CD (ps/nm):                8350.42
-  PMD (ps):                  0.89
+  PMD (ps):                  7.99
+  PDL (dB):                  3.74
 
 Transmission result for input power = 2.00 dBm:
   Final GSNR (0.1 nm): 19.27 dB

tests/invocation/transmission_main_example

@@ -14,6 +14,7 @@ Transceiver Site_A
   OSNR ASE (signal bw, dB):  35.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Fiber          Span1
   type_variety:                SSMF
   length (km):                 80.00
@@ -42,6 +43,7 @@ Transceiver Site_B
   OSNR ASE (signal bw, dB):  29.21
   CD (ps/nm):                1336.00
   PMD (ps):                  0.36
+  PDL (dB):                  0.00
 
 Transmission result for input power = 0.00 dBm:
   Final GSNR (0.1 nm): 31.17 dB

tests/invocation/transmission_main_example__raman

@@ -14,6 +14,7 @@ Transceiver Site_A
   OSNR ASE (signal bw, dB):  35.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 RamanFiber          Span1
   type_variety:                SSMF
   length (km):                 80.00
@@ -21,109 +22,112 @@ RamanFiber          Span1
   total loss (dB):             17.00
   (includes conn loss (dB) in: 0.50 out: 0.50)
   (conn loss out includes EOL margin defined in eqpt_config.json)
-  pch out (dBm): -7.74
+  pch out (dBm): -7.71
+Fused Fused1
+  loss (dB): 0.00
 Edfa Edfa1
   type_variety:           std_low_gain
-  effective gain(dB):     5.74
+  effective gain(dB):     5.71
   (before att_in and before output VOA)
-  noise figure (dB):      13.26
+  noise figure (dB):      13.29
   (including att_in)
-  pad att_in (dB):        2.26
-  Power In (dBm):         11.07
+  pad att_in (dB):        2.29
+  Power In (dBm):         11.11
   Power Out (dBm):        16.82
   Delta_P (dB):           -2.00
   target pch (dBm):       -2.00
   effective pch (dBm):    -2.00
   output VOA (dB):        0.00
 Transceiver Site_B
-  GSNR (0.1nm, dB):          31.43
-  GSNR (signal bw, dB):      27.35
-  OSNR ASE (0.1nm, dB):      34.18
-  OSNR ASE (signal bw, dB):  30.10
+  GSNR (0.1nm, dB):          31.44
+  GSNR (signal bw, dB):      27.36
+  OSNR ASE (0.1nm, dB):      34.22
+  OSNR ASE (signal bw, dB):  30.14
   CD (ps/nm):                1336.00
   PMD (ps):                  0.36
+  PDL (dB):                  0.00
 
 Transmission result for input power = 0.00 dBm:
-  Final GSNR (0.1 nm): 31.43 dB
+  Final GSNR (0.1 nm): 31.44 dB
 
 The GSNR per channel at the end of the line is:
 Ch. #   Channel frequency (THz)       Channel power (dBm)    OSNR ASE (signal bw, dB)     SNR NLI (signal bw, dB)        GSNR (signal bw, dB)
-    1                    191.35                      0.21                       31.56                       31.47                       28.50
-    2                    191.40                      0.17                       31.54                       31.38                       28.45
-    3                    191.45                      0.14                       31.52                       31.30                       28.40
-    4                    191.50                      0.10                       31.50                       31.22                       28.34
-    5                    191.55                      0.04                       31.47                       31.14                       28.29
-    6                    191.60                     -0.02                       31.44                       31.06                       28.23
-    7                    191.65                     -0.08                       31.41                       30.98                       28.18
-    8                    191.70                     -0.14                       31.37                       30.90                       28.12
-    9                    191.75                     -0.20                       31.34                       30.83                       28.07
-   10                    191.80                     -0.26                       31.31                       30.75                       28.01
-   11                    191.85                     -0.33                       31.27                       30.68                       27.96
-   12                    191.90                     -0.39                       31.24                       30.61                       27.90
-   13                    191.95                     -0.46                       31.20                       30.54                       27.85
-   14                    192.00                     -0.52                       31.17                       30.47                       27.79
-   15                    192.05                     -0.59                       31.13                       30.40                       27.74
-   16                    192.10                     -0.66                       31.10                       30.33                       27.69
-   17                    192.15                     -0.72                       31.06                       30.26                       27.63
-   18                    192.20                     -0.79                       31.02                       30.20                       27.58
-   19                    192.25                     -0.86                       30.98                       30.21                       27.57
-   20                    192.30                     -0.94                       30.94                       30.21                       27.55
-   21                    192.35                     -1.01                       30.90                       30.22                       27.54
-   22                    192.40                     -1.09                       30.86                       30.23                       27.52
-   23                    192.45                     -1.16                       30.81                       30.23                       27.50
-   24                    192.50                     -1.24                       30.77                       30.24                       27.49
-   25                    192.55                     -1.31                       30.73                       30.25                       27.47
-   26                    192.60                     -1.38                       30.69                       30.25                       27.46
-   27                    192.65                     -1.45                       30.65                       30.26                       27.44
-   28                    192.70                     -1.52                       30.61                       30.27                       27.42
-   29                    192.75                     -1.59                       30.56                       30.28                       27.41
-   30                    192.80                     -1.66                       30.52                       30.28                       27.39
-   31                    192.85                     -1.73                       30.48                       30.29                       27.37
-   32                    192.90                     -1.80                       30.44                       30.30                       27.36
-   33                    192.95                     -1.87                       30.39                       30.30                       27.34
-   34                    193.00                     -1.94                       30.35                       30.31                       27.32
-   35                    193.05                     -2.01                       30.31                       30.32                       27.30
-   36                    193.10                     -2.08                       30.27                       30.33                       27.29
-   37                    193.15                     -2.15                       30.22                       30.33                       27.27
-   38                    193.20                     -2.22                       30.18                       30.35                       27.25
-   39                    193.25                     -2.29                       30.14                       30.37                       27.24
-   40                    193.30                     -2.36                       30.09                       30.39                       27.23
-   41                    193.35                     -2.43                       30.05                       30.40                       27.21
-   42                    193.40                     -2.49                       30.01                       30.42                       27.20
-   43                    193.45                     -2.56                       29.96                       30.44                       27.18
-   44                    193.50                     -2.63                       29.92                       30.46                       27.17
-   45                    193.55                     -2.70                       29.87                       30.47                       27.15
-   46                    193.60                     -2.78                       29.83                       30.49                       27.13
-   47                    193.65                     -2.85                       29.78                       30.51                       27.12
-   48                    193.70                     -2.92                       29.73                       30.53                       27.10
-   49                    193.75                     -2.99                       29.68                       30.54                       27.08
-   50                    193.80                     -3.06                       29.64                       30.56                       27.06
-   51                    193.85                     -3.14                       29.59                       30.58                       27.05
-   52                    193.90                     -3.21                       29.54                       30.60                       27.03
-   53                    193.95                     -3.28                       29.49                       30.62                       27.01
-   54                    194.00                     -3.35                       29.44                       30.64                       26.99
-   55                    194.05                     -3.42                       29.39                       30.65                       26.97
-   56                    194.10                     -3.50                       29.34                       30.67                       26.95
-   57                    194.15                     -3.57                       29.29                       30.73                       26.94
-   58                    194.20                     -3.64                       29.24                       30.79                       26.94
-   59                    194.25                     -3.72                       29.19                       30.85                       26.93
-   60                    194.30                     -3.79                       29.14                       30.91                       26.93
-   61                    194.35                     -3.86                       29.09                       30.97                       26.92
-   62                    194.40                     -3.93                       29.04                       31.03                       26.91
-   63                    194.45                     -4.01                       28.99                       31.09                       26.90
-   64                    194.50                     -4.08                       28.94                       31.15                       26.90
-   65                    194.55                     -4.14                       28.89                       31.22                       26.89
-   66                    194.60                     -4.21                       28.85                       31.28                       26.88
-   67                    194.65                     -4.28                       28.80                       31.35                       26.88
-   68                    194.70                     -4.34                       28.75                       31.41                       26.87
-   69                    194.75                     -4.41                       28.70                       31.48                       26.86
-   70                    194.80                     -4.47                       28.66                       31.55                       26.86
-   71                    194.85                     -4.54                       28.61                       31.62                       26.85
-   72                    194.90                     -4.60                       28.56                       31.69                       26.84
-   73                    194.95                     -4.67                       28.51                       31.77                       26.83
-   74                    195.00                     -4.73                       28.47                       31.84                       26.82
-   75                    195.05                     -4.80                       28.42                       31.91                       26.81
-   76                    195.10                     -4.86                       28.37                       31.91                       26.78
+    1                    191.35                      0.18                       31.61                       31.43                       28.51
+    2                    191.40                      0.14                       31.59                       31.34                       28.45
+    3                    191.45                      0.11                       31.57                       31.26                       28.40
+    4                    191.50                      0.07                       31.55                       31.17                       28.35
+    5                    191.55                      0.02                       31.52                       31.09                       28.29
+    6                    191.60                     -0.04                       31.49                       31.02                       28.24
+    7                    191.65                     -0.10                       31.46                       30.94                       28.18
+    8                    191.70                     -0.16                       31.43                       30.86                       28.13
+    9                    191.75                     -0.21                       31.40                       30.79                       28.07
+   10                    191.80                     -0.28                       31.36                       30.71                       28.02
+   11                    191.85                     -0.34                       31.33                       30.64                       27.96
+   12                    191.90                     -0.40                       31.29                       30.57                       27.91
+   13                    191.95                     -0.47                       31.26                       30.50                       27.85
+   14                    192.00                     -0.53                       31.22                       30.43                       27.80
+   15                    192.05                     -0.60                       31.18                       30.36                       27.74
+   16                    192.10                     -0.66                       31.15                       30.30                       27.69
+   17                    192.15                     -0.73                       31.11                       30.23                       27.64
+   18                    192.20                     -0.79                       31.07                       30.16                       27.58
+   19                    192.25                     -0.86                       31.04                       30.17                       27.57
+   20                    192.30                     -0.94                       30.99                       30.18                       27.56
+   21                    192.35                     -1.01                       30.95                       30.19                       27.54
+   22                    192.40                     -1.08                       30.91                       30.20                       27.53
+   23                    192.45                     -1.16                       30.86                       30.20                       27.51
+   24                    192.50                     -1.23                       30.82                       30.21                       27.49
+   25                    192.55                     -1.30                       30.78                       30.22                       27.48
+   26                    192.60                     -1.37                       30.74                       30.23                       27.46
+   27                    192.65                     -1.44                       30.70                       30.23                       27.45
+   28                    192.70                     -1.51                       30.65                       30.24                       27.43
+   29                    192.75                     -1.58                       30.61                       30.25                       27.42
+   30                    192.80                     -1.65                       30.57                       30.26                       27.40
+   31                    192.85                     -1.72                       30.53                       30.27                       27.38
+   32                    192.90                     -1.79                       30.48                       30.27                       27.37
+   33                    192.95                     -1.86                       30.44                       30.28                       27.35
+   34                    193.00                     -1.93                       30.40                       30.29                       27.33
+   35                    193.05                     -2.00                       30.35                       30.30                       27.32
+   36                    193.10                     -2.07                       30.31                       30.30                       27.30
+   37                    193.15                     -2.14                       30.27                       30.31                       27.28
+   38                    193.20                     -2.20                       30.22                       30.33                       27.27
+   39                    193.25                     -2.27                       30.18                       30.35                       27.25
+   40                    193.30                     -2.34                       30.14                       30.37                       27.24
+   41                    193.35                     -2.41                       30.09                       30.38                       27.23
+   42                    193.40                     -2.48                       30.05                       30.40                       27.21
+   43                    193.45                     -2.55                       30.00                       30.42                       27.20
+   44                    193.50                     -2.61                       29.96                       30.44                       27.18
+   45                    193.55                     -2.69                       29.91                       30.46                       27.16
+   46                    193.60                     -2.76                       29.86                       30.47                       27.15
+   47                    193.65                     -2.83                       29.82                       30.49                       27.13
+   48                    193.70                     -2.90                       29.77                       30.51                       27.11
+   49                    193.75                     -2.98                       29.72                       30.53                       27.10
+   50                    193.80                     -3.05                       29.67                       30.55                       27.08
+   51                    193.85                     -3.12                       29.62                       30.57                       27.06
+   52                    193.90                     -3.19                       29.58                       30.58                       27.04
+   53                    193.95                     -3.26                       29.53                       30.60                       27.02
+   54                    194.00                     -3.33                       29.48                       30.62                       27.00
+   55                    194.05                     -3.41                       29.43                       30.64                       26.98
+   56                    194.10                     -3.48                       29.38                       30.66                       26.96
+   57                    194.15                     -3.55                       29.33                       30.72                       26.96
+   58                    194.20                     -3.63                       29.28                       30.78                       26.95
+   59                    194.25                     -3.70                       29.23                       30.83                       26.95
+   60                    194.30                     -3.77                       29.18                       30.89                       26.94
+   61                    194.35                     -3.85                       29.12                       30.96                       26.93
+   62                    194.40                     -3.92                       29.07                       31.02                       26.93
+   63                    194.45                     -3.99                       29.02                       31.08                       26.92
+   64                    194.50                     -4.06                       28.97                       31.14                       26.91
+   65                    194.55                     -4.13                       28.92                       31.21                       26.91
+   66                    194.60                     -4.19                       28.88                       31.27                       26.90
+   67                    194.65                     -4.26                       28.83                       31.34                       26.89
+   68                    194.70                     -4.33                       28.78                       31.41                       26.89
+   69                    194.75                     -4.39                       28.73                       31.47                       26.88
+   70                    194.80                     -4.46                       28.68                       31.54                       26.87
+   71                    194.85                     -4.52                       28.64                       31.61                       26.86
+   72                    194.90                     -4.59                       28.59                       31.68                       26.86
+   73                    194.95                     -4.65                       28.54                       31.76                       26.85
+   74                    195.00                     -4.72                       28.49                       31.83                       26.84
+   75                    195.05                     -4.78                       28.44                       31.91                       26.83
+   76                    195.10                     -4.85                       28.39                       31.91                       26.79
 
 (No source node specified: picked Site_A)
 

tests/invocation/transmission_saturated

@@ -250,6 +250,7 @@ Transceiver trx Lannion_CAS
   OSNR ASE (signal bw, dB):  95.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm Lannion_CAS
   effective loss (dB):  23.00
   pch out (dBm):        -20.00
@@ -317,6 +318,7 @@ Transceiver trx Lorient_KMA
   OSNR ASE (signal bw, dB):  20.20
   CD (ps/nm):                2171.00
   PMD (ps):                  0.46
+  PDL (dB):                  0.00
 
 Transmission result for input power = 3.00 dBm:
   Final GSNR (0.1 nm): 23.94 dB

tests/test_amplifier.py

@@ -80,13 +80,12 @@ def si(nch_and_spacing, bw):
 def test_variable_gain_nf(gain, nf_expected, setup_edfa_variable_gain, si):
     """=> unitary test for variable gain model Edfa._calc_nf() (and Edfa.interpol_params)"""
     edfa = setup_edfa_variable_gain
-    frequencies = array([c.frequency for c in si.carriers])
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
-    pin = pin / db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
+    si.signal /= db2lin(gain)
+    si.nli /= db2lin(gain)
+    si.ase /= db2lin(gain)
     edfa.operational.gain_target = gain
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
+    edfa.interpol_params(si)
     result = edfa.nf
     assert pytest.approx(nf_expected, abs=0.01) == result[0]
 
@@ -95,23 +94,20 @@ def test_variable_gain_nf(gain, nf_expected, setup_edfa_variable_gain, si):
 def test_fixed_gain_nf(gain, nf_expected, setup_edfa_fixed_gain, si):
     """=> unitary test for fixed gain model Edfa._calc_nf() (and Edfa.interpol_params)"""
     edfa = setup_edfa_fixed_gain
-    frequencies = array([c.frequency for c in si.carriers])
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
-    pin = pin / db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
+    si.signal /= db2lin(gain)
+    si.nli /= db2lin(gain)
+    si.ase /= db2lin(gain)
     edfa.operational.gain_target = gain
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
-
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
+    edfa.interpol_params(si)
     assert pytest.approx(nf_expected, abs=0.01) == edfa.nf[0]
 
 
 def test_si(si, nch_and_spacing):
     """basic total power check of the channel comb generation"""
     nb_channel = nch_and_spacing[0]
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
-    p_tot = pin.sum()
-    expected_p_tot = si.carriers[0].power.signal * nb_channel
+    p_tot = sum(si.signal + si.ase + si.nli)
+    expected_p_tot = si.signal[0] * nb_channel
     assert pytest.approx(expected_p_tot, abs=0.01) == p_tot
 
 
@@ -122,14 +118,13 @@ def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
      between gain_min and gain_flatmax some discrepancy is expected but target < 0.5dB
      => unitary test for Edfa._calc_nf (and Edfa.interpol_params)"""
     edfa = setup_edfa_variable_gain
-    frequencies = array([c.frequency for c in si.carriers])
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
-    pin = pin / db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
+    si.signal /= db2lin(gain)
+    si.nli /= db2lin(gain)
+    si.ase /= db2lin(gain)
     edfa.operational.gain_target = gain
     # edfa is variable gain type
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
+    edfa.interpol_params(si)
     nf_model = edfa.nf[0]
 
     # change edfa type variety to a polynomial
@@ -155,7 +150,7 @@ def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
     edfa = Edfa(**el_config)
 
     # edfa is variable gain type
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    edfa.interpol_params(si)
     nf_poly = edfa.nf[0]
     print(nf_poly, nf_model)
     assert pytest.approx(nf_model, abs=0.5) == nf_poly
@@ -183,21 +178,16 @@ def test_ase_noise(gain, si, setup_trx, bw):
     si = span(si)
     print(span)
 
-    frequencies = array([c.frequency for c in si.carriers])
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
-    baud_rates = array([c.baud_rate for c in si.carriers])
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
+    edfa.interpol_params(si)
     nf = edfa.nf
     print('nf', nf)
-    pin = lin2db(pin[0] * 1e3)
+    pin = lin2db((si.signal[0] + si.ase[0] + si.nli[0]) * 1e3)
     osnr_expected = pin - nf[0] + 58
 
     si = edfa(si)
     print(edfa)
-    pout = array([c.power.signal for c in si.carriers])
-    pase = array([c.power.ase for c in si.carriers])
-    osnr = lin2db(pout[0] / pase[0]) - lin2db(12.5e9 / bw)
+    osnr = lin2db(si.signal[0] / si.ase[0]) - lin2db(12.5e9 / bw)
     assert pytest.approx(osnr_expected, abs=0.01) == osnr
 
     trx = setup_trx

tests/test_info.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import pytest
+from numpy import array, zeros, ones
+from numpy.testing import assert_array_equal
+
+from gnpy.core.info import create_arbitrary_spectral_information
+from gnpy.core.exceptions import SpectrumError
+
+
+def test_create_arbitrary_spectral_information():
+    si = create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12],
+                                               baud_rate=32e9, signal=[1, 1, 1])
+    assert_array_equal(si.baud_rate, array([32e9, 32e9, 32e9]))
+    assert_array_equal(si.slot_width, array([37.5e9, 37.5e9, 37.5e9]))
+    assert_array_equal(si.signal, ones(3))
+    assert_array_equal(si.nli, zeros(3))
+    assert_array_equal(si.ase, zeros(3))
+    assert_array_equal(si.roll_off, zeros(3))
+    assert_array_equal(si.chromatic_dispersion, zeros(3))
+    assert_array_equal(si.pmd, zeros(3))
+    assert_array_equal(si.channel_number, array([1, 2, 3]))
+    assert_array_equal(si.number_of_channels, 3)
+    assert_array_equal(si.df, array([[0, 50e9, 100e9], [-50e9, 0, 50e9], [-100e9, -50e9, 0]]))
+
+    with pytest.raises(SpectrumError, match='Spectra cannot be summed: channels overlapping.'):
+        si += si
+
+    si = create_arbitrary_spectral_information(frequency=array([193.35e12, 193.3e12, 193.25e12]),
+                                               slot_width=array([50e9, 50e9, 50e9]),
+                                               baud_rate=32e9, signal=array([1, 2, 3]))
+    assert_array_equal(si.signal, array([3, 2, 1]))
+
+    with pytest.raises(SpectrumError, match='Spectrum baud rate, including the roll off, '
+                                            r'larger than the slot width for channels: \[1, 3\].'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=1,
+                                              baud_rate=[64e9, 32e9, 64e9], slot_width=50e9)
+    with pytest.raises(SpectrumError, match='Spectrum required slot widths larger than the frequency spectral '
+                                            r'distances between channels: \[\(1, 2\), \(3, 4\)\].'):
+        create_arbitrary_spectral_information(frequency=[193.26e12, 193.3e12, 193.35e12, 193.39e12], signal=1,
+                                              baud_rate=32e9, slot_width=50e9)
+    with pytest.raises(SpectrumError, match='Spectrum required slot widths larger than the frequency spectral '
+                                            r'distances between channels: \[\(1, 2\), \(2, 3\)\].'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=1, baud_rate=49e9,
+                                              roll_off=0.1)
+
+    with pytest.raises(SpectrumError,
+                       match='Dimension mismatch in input fields.'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=[1, 2], baud_rate=49e9)

tests/test_parameters.py

@@ -1,26 +1,23 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-from pathlib import Path
+"""
+Checks that the class SimParams behaves as a mutable Singleton.
+"""
 
+import pytest
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
-from gnpy.tools.json_io import load_json
-
-TEST_DIR = Path(__file__).parent
-DATA_DIR = TEST_DIR / 'data'
 
 
+@pytest.mark.usefixtures('set_sim_params')
 def test_sim_parameters():
-    j = load_json(DATA_DIR / 'sim_params.json')
-    sim_params = SimParams(**j)
-    Simulation.set_params(sim_params)
-    s1 = Simulation.get_simulation()
-    assert s1.sim_params.raman_params.flag_raman
-    s2 = Simulation.get_simulation()
-    assert s2.sim_params.raman_params.flag_raman
-    j['raman_parameters']['flag_raman'] = False
-    sim_params = SimParams(**j)
-    Simulation.set_params(sim_params)
-    assert not s2.sim_params.raman_params.flag_raman
-    assert not s1.sim_params.raman_params.flag_raman
+    sim_params = {'nli_params': {}, 'raman_params': {}}
+    SimParams.set_params(sim_params)
+    s1 = SimParams.get()
+    assert s1.nli_params.method == 'gn_model_analytic'
+    s2 = SimParams.get()
+    assert not s1.raman_params.flag
+    sim_params['raman_params']['flag'] = True
+    SimParams.set_params(sim_params)
+    assert s2.raman_params.flag
+    assert s1.raman_params.flag

tests/test_parser.py

@@ -21,7 +21,6 @@ import shutil
 from pandas import read_csv
 from xlrd import open_workbook
 import pytest
-from tests.compare import compare_networks, compare_services
 from copy import deepcopy
 from gnpy.core.utils import automatic_nch, lin2db
 from gnpy.core.network import build_network
@@ -56,15 +55,7 @@ def test_excel_json_generation(tmpdir, xls_input, expected_json_output):
     actual_json_output = xls_copy.with_suffix('.json')
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(expected_json_output)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
+    assert actual == load_json(expected_json_output)
 
 # assume xls entries
 # test that the build network gives correct results in gain mode
@@ -95,15 +86,7 @@ def test_auto_design_generation_fromxlsgainmode(tmpdir, xls_input, expected_json
     save_network(network, actual_json_output)
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(expected_json_output)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
+    assert actual == load_json(expected_json_output)
 
 # test that autodesign creates same file as an input file already autodesigned
 
@@ -134,15 +117,7 @@ def test_auto_design_generation_fromjson(tmpdir, json_input, power_mode):
     save_network(network, actual_json_output)
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(json_input)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
+    assert actual == load_json(json_input)
 
 # test services creation
 
@@ -162,15 +137,7 @@ def test_excel_service_json_generation(xls_input, expected_json_output):
                                              equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
     build_network(network, equipment, p_db, p_total_db)
     from_xls = read_service_sheet(xls_input, equipment, network, network_filename=DATA_DIR / 'testTopology.xls')
-    expected = load_json(expected_json_output)
-
-    results = compare_services(expected, from_xls)
-    assert not results.requests.missing
-    assert not results.requests.extra
-    assert not results.requests.different
-    assert not results.synchronizations.missing
-    assert not results.synchronizations.extra
-    assert not results.synchronizations.different
+    assert from_xls == load_json(expected_json_output)
 
     # TODO verify that requested bandwidth is not zero !
 
@@ -391,6 +358,7 @@ def test_excel_ila_constraints(source, destination, route_list, hoptype, expecte
         'cost': None,
         'roll_off': 0,
         'tx_osnr': 0,
+        'penalties': None,
         'min_spacing': None,
         'nb_channel': 0,
         'power': 0,

tests/test_roadm_restrictions.py

@@ -12,6 +12,8 @@ checks that restrictions in roadms are correctly applied during autodesign
 
 from pathlib import Path
 import pytest
+from numpy.testing import assert_allclose
+
 from gnpy.core.utils import lin2db, automatic_nch
 from gnpy.core.elements import Fused, Roadm, Edfa
 from gnpy.core.network import build_network
@@ -254,10 +256,11 @@ def test_roadm_target_power(prev_node_type, effective_pch_out_db, power_dbm):
         req.power, req.spacing)
     for i, el in enumerate(path):
         if isinstance(el, Roadm):
-            carriers_power_in_roadm = min([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+            min_power_in_roadm = min(si.signal + si.ase + si.nli)
             si = el(si, degree=path[i + 1].uid)
+            power_out_roadm = si.signal + si.ase + si.nli
             if el.uid == 'roadm node B':
-                print('input', carriers_power_in_roadm)
+                print('input', min_power_in_roadm)
                 # if previous was an EDFA, power level at ROADM input is enough for the ROADM to apply its
                 # target power (as specified in equipment ie -20 dBm)
                 # if it is a Fused, the input power to the ROADM is smaller than the target power, and the
@@ -266,23 +269,17 @@ def test_roadm_target_power(prev_node_type, effective_pch_out_db, power_dbm):
                 # corresponds to -22dBm + power_dbm
                 # next step (for ROADM modelling) will be to apply a minimum loss for ROADMs !
                 if prev_node_type == 'edfa':
-                    assert el.effective_pch_out_db == effective_pch_out_db
-                if prev_node_type == 'fused':
-                    # then output power == input_power == effective_pch_out_db + power_dbm
-                    assert effective_pch_out_db + power_dbm == \
-                        pytest.approx(lin2db(carriers_power_in_roadm * 1e3), rel=1e-3)
-                    assert el.effective_pch_out_db == effective_pch_out_db + power_dbm
-                for carrier in si.carriers:
-                    print(carrier.power.signal + carrier.power.nli + carrier.power.ase)
-                    power = carrier.power.signal + carrier.power.nli + carrier.power.ase
-                    if prev_node_type == 'edfa':
-                        # edfa prev_node sets input power to roadm to a high enough value:
-                        # Check that egress power of roadm is equal to target power
-                        assert power == pytest.approx(db2lin(effective_pch_out_db - 30), rel=1e-3)
-                    elif prev_node_type == 'fused':
-                        # fused prev_node does reamplfy power after fiber propagation, so input power
-                        # to roadm is low.
-                        # Check that egress power of roadm is equalized to the min carrier input power.
-                        assert power == pytest.approx(carriers_power_in_roadm, rel=1e-3)
+                    # edfa prev_node sets input power to roadm to a high enough value:
+                    # check that target power is correctly set in the ROADM
+                    assert_allclose(el.pch_out_db, effective_pch_out_db, rtol=1e-3)
+                    # Check that egress power of roadm is equal to target power
+                    assert_allclose(power_out_roadm, db2lin(effective_pch_out_db - 30), rtol=1e-3)
+                elif prev_node_type == 'fused':
+                    # fused prev_node does reamplfy power after fiber propagation, so input power
+                    # to roadm is low.
+                    # check that target power correctly reports power_dbm from previous propagation
+                    assert_allclose(el.pch_out_db, effective_pch_out_db + power_dbm, rtol=1e-3)
+                    # Check that egress power of roadm is equalized to the min carrier input power.
+                    assert_allclose(power_out_roadm, min_power_in_roadm, rtol=1e-3)
         else:
             si = el(si)

tests/test_science_utils.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# @Author: Alessio Ferrari
+
 """
 Checks that RamanFiber propagates properly the spectral information. In this way, also the RamanSolver and the NliSolver
 are tested.
@@ -9,40 +9,120 @@ are tested.
 from pathlib import Path
 from pandas import read_csv
 from numpy.testing import assert_allclose
+from numpy import array, genfromtxt
+import pytest
 
-from gnpy.core.info import create_input_spectral_information
-from gnpy.core.elements import RamanFiber
+from gnpy.core.info import create_input_spectral_information, create_arbitrary_spectral_information
+from gnpy.core.elements import Fiber, RamanFiber
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
 from gnpy.tools.json_io import load_json
+from gnpy.core.exceptions import NetworkTopologyError
+from gnpy.core.science_utils import RamanSolver
 
 TEST_DIR = Path(__file__).parent
 
 
-def test_raman_fiber():
-    """ Test the accuracy of propagating the RamanFiber."""
-    # spectral information generation
-    power = 1e-3
-    eqpt_params = load_json(TEST_DIR / 'data' / 'eqpt_config.json')
-    spectral_info_params = eqpt_params['SI'][0]
-    spectral_info_params.pop('power_dbm')
-    spectral_info_params.pop('power_range_db')
-    spectral_info_params.pop('tx_osnr')
-    spectral_info_params.pop('sys_margins')
-    spectral_info_input = create_input_spectral_information(power=power, **spectral_info_params)
+def test_fiber():
+    """ Test the accuracy of propagating the Fiber."""
+    fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
 
-    sim_params = SimParams(**load_json(TEST_DIR / 'data' / 'sim_params.json'))
-    Simulation.set_params(sim_params)
-    fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'raman_fiber_config.json'))
+    # fix grid spectral information generation
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+    # propagation
+    spectral_info_out = fiber(spectral_info_input)
+
+    p_signal = spectral_info_out.signal
+    p_nli = spectral_info_out.nli
+
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_fix_expected_results.csv')
+    assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
+    assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+    # flex grid spectral information generation
+    frequency = 191e12 + array([0, 50e9, 150e9, 225e9, 275e9])
+    slot_width = array([37.5e9, 50e9, 75e9, 50e9, 37.5e9])
+    baud_rate = array([32e9, 42e9, 64e9, 42e9, 32e9])
+    signal = 1e-3 + array([0, -1e-4, 3e-4, -2e-4, +2e-4])
+    spectral_info_input = create_arbitrary_spectral_information(frequency=frequency, slot_width=slot_width,
+                                                                signal=signal, baud_rate=baud_rate, roll_off=0.15)
 
     # propagation
     spectral_info_out = fiber(spectral_info_input)
 
-    p_signal = [carrier.power.signal for carrier in spectral_info_out.carriers]
-    p_ase = [carrier.power.ase for carrier in spectral_info_out.carriers]
-    p_nli = [carrier.power.nli for carrier in spectral_info_out.carriers]
+    p_signal = spectral_info_out.signal
+    p_nli = spectral_info_out.nli
 
-    expected_results = read_csv(TEST_DIR / 'data' / 'test_science_utils_expected_results.csv')
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_results.csv')
+    assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
+    assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+
+@pytest.mark.usefixtures('set_sim_params')
+def test_raman_fiber():
+    """ Test the accuracy of propagating the RamanFiber."""
+    # spectral information generation
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
+
+    # propagation
+    spectral_info_out = fiber(spectral_info_input)
+
+    p_signal = spectral_info_out.signal
+    p_ase = spectral_info_out.ase
+    p_nli = spectral_info_out.nli
+
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_raman_fiber_expected_results.csv')
     assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
     assert_allclose(p_ase, expected_results['ase'], rtol=1e-3)
     assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+
+@pytest.mark.parametrize(
+    "loss, position, errmsg",
+    ((0.5, -2, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded."),
+     (0.5, 81, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded.")))
+@pytest.mark.usefixtures('set_sim_params')
+def test_fiber_lumped_losses(loss, position, errmsg, set_sim_params):
+    """ Lumped losses length sanity checking."""
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber_dict = load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')
+    fiber_dict['params']['lumped_losses'] = [{'position': position, 'loss': loss}]
+    with pytest.raises(NetworkTopologyError) as e:
+        Fiber(**fiber_dict)
+    assert str(e.value) == errmsg
+
+
+@pytest.mark.usefixtures('set_sim_params')
+def test_fiber_lumped_losses_srs(set_sim_params):
+    """ Test the accuracy of Fiber with lumped losses propagation."""
+    # spectral information generation
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
+    raman_fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
+
+    # propagation
+    # without Raman pumps
+    stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
+        spectral_info_input, fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv', delimiter=',')
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
+
+    # with Raman pumps
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv', delimiter=',')
+    stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
+        spectral_info_input, raman_fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
+
+    # without Stimulated Raman Scattering
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', delimiter=',')
+    stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info_input, fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)

tests/test_spectrum_assignment.py

@@ -291,6 +291,7 @@ def request_set():
         'cost': 1,
         'roll_off': 0.15,
         'tx_osnr': 38,
+        'penalties': {},
         'min_spacing': 37.5e9,
         'nb_channel': None,
         'power': 0,

tox.ini

@@ -4,7 +4,7 @@ skipsdist = True
 [testenv]
 deps =
 	-r{toxinidir}/requirements.txt
-	pytest>=5.0.0,<6
+	pytest>=6.2.5,<7
 	cover: pytest-cov
 	linters: flake8
 	linters: pep8-naming
@@ -43,3 +43,4 @@ commands =
 [flake8]
 max-line-length = 120
 max-complexity = 15
+ignore = N806